Section 4.0 - Basic Writeup
Section 4.1 - Dimensionality Reduction - Derived From Structural Math and Statistics
Section 4.1.0 - Basic Writeup - Wikipedia - Dimensionality Reduction
Section 4.1.1 - Feature Selection
Section 4.1.1.0 - Basic Writeup - Wikipedia - Feature Selection
Section 4.1.1.1 - Filter method (Also known as Structured Learning)
Section 4.1.1.2 - Wrapper method
Section 4.1.1.2.0 - Introductory Writeups
Section 4.1.1.2.0.1 - Basic Writeup - Sebastian Raschka - Machine Learning FAQ: Different Methods of Feature Selection
Section 4.1.1.2.0.2 - Detailed Writeup - Kohavi, John - Wrappers for feature subset selection
Section 4.1.1.3 - Embedded method
Section 4.1.1.3.0 - Introductory Writeups
Section 4.1.1.3.0.1 - Basic Writeup - Analytics Vidhya - Introduction to Feature Selection Methods with Examples
Section 4.1.1.3.0.2 - Detailed Writeup - Lal, Chapelle, Weston, Elisseeff - Embedded Methods
Section 4.1.2 - Feature Reduction(Extraction,Projection)
Section 4.1.2.0 - Basic Writeup - Wikipedia - Feature Extraction
Section 4.1.2.1 - Linear dimensionality Reduction Technique
Section 4.1.2.1.0 - Basic Writeup - Cunningham,Ghahramani - Linear Dimensionality Reduction: Survey, Insights and Generalizations
Section 4.1.2.1.1 - Principal component analysis (PCA) - Unsupervised
Section 4.1.2.1.1.0 - Introductory Writeups
Section 4.1.2.1.1.0.1 - Basic Writeup - Wikipedia - Principal Component Analysis
Section 4.1.2.1.1.0.2 - Detailed Writeup - Abdi. Williams - Principal Component Analysis
Section 4.1.2.1.1.1 - Robust Principal Component Analysis (rPCA)
Section 4.1.2.1.1.2 - Kernel Principal Component Analysis
Section 4.1.2.1.1.3 - Multilinear Principal Component Analysis
Section 4.1.2.1.2 - Independent Component Analysis (ICA)
Section 4.1.2.1.2.0 - Introductory Writeups
Section 4.1.2.1.2.0.1 - Basic Writeup - Wikipedia - Independent Component Analysis
Section 4.1.2.1.2.0.2 - Detailed Writeup - Izenman - What is Independent Component Analysis?
Section 4.1.2.1.2.1 - Linear noiseless ICA
Section 4.1.2.1.2.2 - Linear noisy ICA
Section 4.1.2.1.2.3 - Nonlinear ICA
Section 4.1.2.1.2.4 - Linear and Nonlinear Mixtures - MISEP ICA
Section 4.1.2.1.3 - Fishers Linear Discriminant - Supervised
Section 4.1.2.1.3.0 - Basic Writeup - Jianxin Wu - Fishers's Linear Discriminant
Section 4.1.2.1.3.1 - Linear Discriminant Analysis
Section 4.1.2.1.3.2 - Generalized(Kernel Fisher) discriminant analysis (GDA)
Section 4.1.2.1.4 - Canonical correlation analysis (CCA)
Section 4.1.2.1.5 - Factor analysis
Section 4.1.2.1.5.0 - Basic Writeup - Wikipedia - Factor Analysis
Section 4.1.2.1.5.1 - Common Factor Analysis
Section 4.1.2.1.5.1.0 - Basic Writeup - Stackexchange - Common Factor Analysis
Section 4.1.2.1.5.1.1 - Principal Axis Factoring (PAF)
Section 4.1.2.1.5.1.1.0 - Basic Writeup - Stackexchange - Principal Axis Factoring (PAF)
Section 4.1.2.1.5.1.1.1 - Principal Factor Analysis
Section 4.1.2.1.5.1.1.2 - Iterative Principal Factor Analysis
Section 4.1.2.1.5.1.2 - Correlation-Fitting Factoring Methods
Section 4.1.2.1.5.1.2.1 - Ordinary Least Squares
Section 4.1.2.1.5.1.2.2 - Generalized Least Squares
Section 4.1.2.1.5.1.2.3 - Maximum Likelihood
Section 4.1.2.1.5.2 - Exploratory factor analysis (EFA)
Section 4.1.2.1.5.2.0 - Introductory Writeups
Section 4.1.2.1.5.2.0.1 - Basic Writeup - Wikipedia - Exploratory Factor Analysis
Section 4.1.2.1.5.2.0.2 - Detailed Writeup - Koostra - Exploratory Factor Analysis
Section 4.1.2.1.5.3 - Confirmatory factor analysis (CFA)
Section 4.1.2.1.6 - Multilinear Subspace Learning
Section 4.1.2.1.7 - Matrix Decomposition(Factorization)
Section 4.1.2.2 - NonLinear dimensionality Reduction Techniques
Section 4.1.2.2.1 - Multidimensional scaling (MDS)
Section 4.1.2.2.2 - Sammon's mapping
Section 4.1.2.2.3 - Self-organizing map
Section 4.1.2.2.4 - Principal curves on manifolds
Section 4.1.2.2.5 - Autoencoders
Section 4.1.2.2.6 - Gaussian process latent variable models
Section 4.1.2.2.7 - ISOMAP
Section 4.1.2.2.8 - Curvilinear component analysis
Section 4.1.2.2.9 - Curvilinear distance analysis
Section 4.1.2.2.10 - Diffeomorphic dimensionality reduction
Section 4.1.2.2.11 - Kernel principal component analysis
Section 4.1.2.2.12 - Contagion maps
Section 4.1.2.2.13 - Locally-linear embedding
Section 4.1.2.2.14 - Laplacian eigenmaps
Section 4.1.2.2.15 - Manifold alignment
Section 4.1.2.2.16 - Diffusion maps
Section 4.1.2.2.17 - Hessian Locally-Linear Embedding (Hessian LLE)
Section 4.1.2.2.18 - Modified Locally-Linear Embedding (MLLE)
Section 4.1.2.2.19 - Relational perspective map
Section 4.1.2.2.20 - Local tangent space alignment
Section 4.1.2.2.21 - Local multidimensional scaling
Section 4.1.2.2.22 - Maximum variance unfolding
Section 4.1.2.2.23 - Nonlinear PCA
Section 4.1.2.2.24 - Data-driven high-dimensional scaling
Section 4.1.2.2.25 - Manifold sculpting
Section 4.1.2.2.26 - t-distributed stochastic neighbor embedding
Section 4.1.2.2.27 - RankVisu
Section 4.1.2.2.28 - Topologically constrained isometric embedding
Section 4.1.2.2.29 - Semidefinite embedding
Section 4.1.2.2.30 - Latent semantic analysis
Section 4.1.2.2.31 - Multifactor dimensionality reduction
Section 4.1.2.3 - Multifactor dimensionality reduction
Section 4.1.2.4 - Semi-Definite Embedding
Section 4.1.2.4 - Autoencoder
Section 4.2 - Artifical Neural Nets - Derived From Neuroscience
Section 4.2.0 - Introductory Writeups
Section 4.2.0.1 - Basic Writeups
Section 4.2.0.1.1 - Part 1 - Wikipedia - What is an ANN?
Section 4.2.0.1.2 - Part 2 - Asimov Institute - Cells and Layers of ANNs
Section 4.2.0.1.3 - Part 3 - Asimov Institute - Some examples of ANNs
Section 4.2.0.2 - Detailed Writeups
Section 4.2.0.2.1 - Part 1 - Textbook - David Kriesel - A Brief Introduction to Neural Networks
Section 4.2.0.2.2 - Part 2 - Textbook - Michael Nielsen - Neural Networks and Deep Learning
Section 4.2.0.2.3 - Part 3 - Edited by Kenji Suzuk - ANN: Architecture and Applications
Section 4.2.0.2.4 - Part 4 - Text Book - Simon Haykin - Neural Networks and Learning Machines
Section 4.2.0.2.5 - Part 5 - Online Text Book - Zhang,Lipton,Li,Smola - Dive Into Deep Learning
Section 4.2.0.2.6 - Part 6 - Complex-Valued Neural Networks
Section 4.2.0.2.6.1 - Part 1 - Trabelsi,Bilaniuk,Zhang,Serdyuk,Subramanian,Santos,Mehri,Rostamzadeh,Bengio, Pal - Deep Complex Networks
Section 4.2.0.2.6.2 - Part 2 - Thesis - Nitzan Guberman - On Complex Valued CNN's
Section 4.2.0.2.6.3 - Part 3 - Monning, Manandhar - Evaluation of Complex-Valued Neural Networks on Real-Valued Classification Tasks
Section 4.2.0.2.6.4 - Part 4 - Monning,Manandhar - Comparison of the Complex Valued and Real Valued Neural Networks Trained with Gradient Descent and Random Search Algorithms
Section 4.2.0.2.6.5 - Part 5 - El-Telbany,Refat - Complex-Valued Neural Networks Training: A Particle Swarm Optimization Strategy
Section 4.2.0.2.6.6 - Part 6 - Aizenberg - Complex-Valued Neural Networks II. Multi-Valued Neurons: Theory and Applications
Section 4.2.0.2.6.7 - Part 7 - Minin,Knoll,Zimmermann - Complex Valued Recurrent Neural Network: From Architecture to Training
Section 4.2.0.2.7 - Part 7 - Li,Xu,Taylot,Studer,Goldstein - Visualizing the Loss Landscape of Neural Networks
Section 4.2.1 - Feedforward Networks
Section 4.2.1.0 - Basic Writeup - Wikipedia - Feed Forward Neural Network
Section 4.2.1.1 - Neural Backprop
Section 4.2.1.1.0 - Basic Writeup - Wikipedia - Neural Back Propogation
Section 4.2.1.1.1 - Backprop
Section 4.2.1.1.1.0 - Introductory Writeups
Section 4.2.1.1.1.0.1 - Basic Writeup - Wikipedia - Backpropogation
Section 4.2.1.1.1.0.2 - Detailed Writeup - Rumelhart,Durbin,Golden,Chauvin - Backpropogation: The Basic Theory
Section 4.2.1.1.1.1 - Delta Rule
Section 4.2.1.1.1.2 - Chain Rule
Section 4.2.1.1.1.3 - Gauss–Newton algorithm
Section 4.2.1.1.2 - Cascade Correlation
Section 4.2.1.1.3 - Quickprop
Section 4.2.1.1.4 - Resilient backprop (RPROP)
Section 4.2.1.2 - Linear and Non-Linear
Section 4.2.1.2.0 - Basic Writeups
Section 4.2.1.2.0.1 - Part 1 - Unknown - Non-Linear Feedforward Control
Section 4.2.1.2.0.2 - Part 2 - Whitman - Linear Neural Networks
Section 4.2.1.2.0.3 - Part 3 - Grossberg - NonLinear Neural Networks
Section 4.2.1.2.1 - Radial Based Function (RBF) networks (Linear Neural Networks)
Section 4.2.1.2.1.0 - Introductory Writeups
Section 4.2.1.2.1.0.1 - Basic Writeup - Wikipedia - Radial Basis Function Network
Section 4.2.1.2.1.0.2 - Detailed Writeup - Orr - Introduction to Radial Based Function Networks
Section 4.2.1.3 - Perceptron
Section 4.2.1.4 - Adaline and Madaline
Section 4.2.1.5 - Higher Order Neural Networks
Section 4.2.1.5.0 - Introductory Writeups
Section 4.2.1.5.0.1 - Basic Writeup
Section 4.2.1.5.0.2 - Detailed Writeup - Gupta,Bukovsky,Homma,Solo,Hou - Fundamentals of Higher Order Neural Networks for Modeling and Simulation
Section 4.2.1.5.1 - Sigma-Pi Networks
Section 4.2.1.5.1.0 - Basic Writeup - Univ of Pretoria - High Order Neural Networks
Section 4.2.1.5.1.1 - Sigma-Pi-Sigma Networks
Section 4.2.1.5.2 - Pi-Sigma Networks
Section 4.2.1.5.2.0 - Basic Writeup - Univ of Pretoria - High Order Neural Networks
Section 4.2.1.5.2.1 - Jordan Pi-Sigma Network
Section 4.2.1.5.3 - Functional Link
Section 4.2.1.5.4 - Second Order
Section 4.2.1.5.5 - Product Unit
Section 4.2.1.6 - MLP: Multilayer perceptron
Section 4.2.1.6.0 - Introductory Writeups
Section 4.2.1.6.0.1 - Basic Writeups
Section 4.2.1.6.0.1.1 - Part 1 - Ujjwalkarn - A Quick Introduction oto Neural Networks
Section 4.2.1.6.0.1.2 - Part 2 - Wikipedia - Multilayer Perceptron
Section 4.2.1.6.0.2 - Detailed Writeup - Textbook Chapter 2.2 - Principe,Euliano,Lefebvre - Multilayer Perceptrons
Section 4.2.1.7 - Convolutional Neural Network
Section 4.2.1.7.0 - Introductory Writeups
Section 4.2.1.7.0.1 - Basic Writeup - Wikipedia - Convolutional Neural Network
Section 4.2.1.7.0.2 - Detailed Writeup - Wu - Convolutional Neural Networks
Section 4.2.1.7.1 - Design of CNN's
Section 4.2.1.7.2 - Building Blocks of CNN's
Section 4.2.1.7.3 - Intuitive Explanations and Demos of CNN's
Section 4.2.1.7.4 - Types of Convolutions
Section 4.2.1.7.4.0 - Basic Writeup - DeepLearning.net - Convolutional Arithmetic
Section 4.2.1.7.4.1 - Discrete Convolutions
Section 4.2.1.7.4.2 - Transposed Convolutions
Section 4.2.1.7.4.3 - Dilated Convolutions
Section 4.2.1.7.4.4 - Grouped Convolutions
Section 4.2.1.7.4.5 - Seperable Convolutions
Section 4.2.1.7.5 - Deep Convolutional Inverse Graphics Network
Section 4.2.1.7.6 - Convolutional Deep Q-networks
Section 4.2.1.7.7 - Convolutional Deep Belief Network
Section 4.2.1.8 - CMAC: Cerebellar Model Articulation Controller
Section 4.2.1.10 - Pure Classification only
Section 4.2.1.10.1 - LVQ: Learning Vector Quantization
Section 4.2.1.10.2 - PNN: Probabilistic Neural Network
Section 4.2.1.11 - Non Parametric Regression
Section 4.2.1.11.1 - GNN: General Regression Neural Network
Section 4.2.1.12 - Hinton Capsule Based
Section 4.2.1.12.0 - Introductory Writeups
Section 4.2.1.12.0.1 - Basic Writeup - Max Pechyonkin - Understanding Hinton's Capsule Networks: Intuition
Section 4.2.1.12.0.2 - Detailed Writeups
Section 4.2.1.12.0.2.1 - Part 1 - Wikipedia - Hinton's Capsule Neural Network
Section 4.2.1.12.0.2.2 - Part 2 - Hinton,Krizhevsky,Wang - Transforming Auto-Encoders
Section 4.2.1.13 - Extreme Learning Machine
Section 4.2.1.14 - Residual Network
Section 4.2.1.14.0 - Basic Writeup - Wikipedia - Residual Neural Network
Section 4.2.1.14.1 - Deep Residual
Section 4.2.1.14.2 - Highway Networks
Section 4.2.2 - Feedback or Recurrent Neural Networks
Section 4.2.2.0 - Basic Writeup - Wikipedia - Recurrent Neural Network
Section 4.2.2.1 - Neural Turing Machine
Section 4.2.2.2 - BAM: Bidirectional Associative Memory
Section 4.2.2.3 - Boltzman Machine
Section 4.2.2.3.0 - Introductory Writeups
Section 4.2.2.3.0.1 - Basic Writeup - Wikipedia - Boltzmann Machine
Section 4.2.2.3.0.2 - Detailed Writeup - Hinton - Boltzmann Machines
Section 4.2.2.3.1 - Restricted Boltzman Machine
Section 4.2.2.3.1.0 - Introductory Writeups
Section 4.2.2.3.1.0.1 - Basic Writeups
Section 4.2.2.3.1.0.1.1 - Part 1 - Skymind.ai - Restricted Boltzmann Machine
Section 4.2.2.3.1.0.1.2 - Part 2 - Wikipedia - Restricted Boltzmann Machine
Section 4.2.2.3.1.0.2 - Detailed Writeups
Section 4.2.2.3.1.0.2.1 - Part 1 - Deeplearning Net - Restricted Boltzmann Machines
Section 4.2.2.3.1.0.2.2 - Part 2 - Mont´ufar - Restricted Boltzmann Machines: Introduction and Review
Section 4.2.2.3.1.1 - Conditional Restricted Boltzman Machine
Section 4.2.2.3.1.2 - Mean Field Theory of Restricted Boltzman Machine
Section 4.2.2.3.1.3 - Deep Belief Networke
Section 4.2.2.3.2 - Deep Boltzman Machine
Section 4.2.2.3.3 - High Order Boltzman Machine
Section 4.2.2.3.4 - Non-Binary Units Boltzman Machine
Section 4.2.2.4 - Gated Recurrent Unit
Section 4.2.2.4.0 - Introductory Writeups
Section 4.2.2.4.0.1 - Basic Writeups
Section 4.2.2.4.0.1.1 - Part 1 - Wikipedia - Gated Recurrent Unit
Section 4.2.2.4.0.1.2 - Part 2 - Zhang,Lipton,Li,Smola - Dive Into Deep Learning: Gated Recurrent Unit
Section 4.2.2.4.0.2 - Detailed Writeup - Chung, Gulcehre, Cho, Bengio - Gated Feedback Recurrent Neural Networks
Section 4.2.2.4.1 - Deep Gate Recurrent Neural Network
Section 4.2.2.5 - Hopfield Network
Section 4.2.2.5.0 - Introductory Writeups
Section 4.2.2.5.0.1 - Basic Writeup - Wikipedia - Hopfield Network
Section 4.2.2.5.0.2 - Detailed Writeup - Textbook Chapter 13 - Rojas - Neural Networks: The Hopfield Model
Section 4.2.2.6 - Recurrent time series
Section 4.2.2.6.0 - Basic Writeup - Petnehazi - Recurrent Neural Networks for Time Series Forecasting
Section 4.2.2.6.1 - Long Short Term Memory (LSTM)
Section 4.2.2.6.1.0 - Introductory Writeups
Section 4.2.2.6.1.0 - Basic Writeups
Section 4.2.2.6.1.0.1.1 - Part 1 - Wikipedia - Long Short Term Memory
Section 4.2.2.6.1.0.1.2 - Part 2 - Skymind.ai - LSTM
Section 4.2.2.6.1.0.2 - Detailed Writeup - Hochreiter,Schmidhuber - Long Short-Term Memory
Section 4.2.2.6.2 - Simple Recurrent Networks
Section 4.2.2.6.2.1 - Elman Network
Section 4.2.2.6.2.2 - Jordan Network
Section 4.2.2.6.3 - FIR: Finite Impulse Response Neural Net
Section 4.2.2.6.4 - Real-time Recurrent Network
Section 4.2.2.6.5 - Recurrent Backprop
Section 4.2.2.6.6 - TDNN: Time Delay NN
Section 4.2.2.7 - Liquid State Machine
Section 4.2.2.8 - Echo State Network
Section 4.2.2.9 - Bidirectional Recurrent Neural Network
Section 4.2.3 - Memory Networks
Section 4.2.3.1 - Attention Mechanism
Section 4.2.3.1.0 - Basic Writeup
Section 4.2.3.1.0.1 - Part 1 - Skymind - Attention Mechanisms
Section 4.2.3.1.0.2 - Part 2 - Mahendra Venkatachalam - Attention In Neural Networks
Section 4.2.3.1.0.3 - Part 3 - Buomsoo Kim - Attention In Neural Networks - Part 1
Section 4.2.3.1.1 - Detailed Writeup - Zhang,Lipton,Li,Smola - Dive into Deep Learning: Attention Mechanism
Section 4.2.3.1.2 - Hierarchical Attention Networks
Section 4.2.3.1.3 - Graph Attention Networks
Section 4.2.3.1.4 - Transformer Attention Networks
Section 4.2.3.1.4.1 - Detailed Writeups
Section 4.2.3.1.4.1.1 - Part 1 - Zhang,Lipton,Li,Smola, - Transformer Attention Mechanism
Section 4.2.3.1.4.1.2 - Part 2 - Vaswani,Shazer,Parmar,Uszkoreit,Jones,Gomez,Kaiser - Attention is All You Need
Section 4.2.3.1.4.1.3 - Part 3 - Devlin,Chang,Lee,Toutanova - Bidirectional Encoder Representations from Transformer Attention Networks(BERT)
Section 4.2.3.1.4.1.4 - Part 4 - Dai,Yang,Yan,Carbonell,Le,Salakhutdinov - Transformer-XL: Attentitive Language Models Beyond a Fixed-Length Context
Section 4.2.3.1.4.1.5 - Part 5 - Yang,Dai,Yang,Carbonell,Salakhutdinov,Le - XLNet: Generalized Autoregressive Pretraining for Language Understanding
Section 4.2.4 - Competitive Networks
Section 4.2.4.0 - Basic Writeup - Wikipedia - Competitive Learning
Section 4.2.4.1 - Adaptive Resonance Theory
Section 4.2.4.2 - Counterpropagation
Section 4.2.4.3- Neocognitron
Section 4.2.4.4 - Vector Quantization
Section 4.2.4.4.0 - Basic Writeup - Vector Quantization
Section 4.2.4.4.1 - Grossberg Competitive Learning
Section 4.2.4.4.2 - Kohonen Self Organizing Maps
Section 4.2.4.4.3 - Conscience Competitive Learning
Section 4.2.4.5 - Self-Organizing Map
Section 4.2.4.5.0 - Basic Writeup - Wikipedia - Self Organizing Map
Section 4.2.4.5.1 - Kohonen Self Organizing Maps
Section 4.2.4.5.2 - Generative Topographics Map (GTM)
Section 4.2.4.5.3 - Local Linear
Section 4.2.4.6 - DCL: Differential Competitive Learning
Section 4.2.5 - Adversarial Networks
Section 4.2.5.1 - Generative Adversarial Networks
Section 4.2.5.1.0 - Introductory Writeups
Section 4.2.5.1.0.1 - Basic Writeups
Section 4.2.5.1.0.1.1 - Part 1 - Geeks For Geeks - Generative Adversarial Networks (GANS)
Section 4.2.5.1.0.1.2 - Part 2 - Fritz.ai - An Introduction to GANS
Section 4.2.5.1.0.1.3 - Part 3 - Hui - GAN Series (from the beginning to the end)
Section 4.2.5.1.0.2 - Detailed Writeups
Section 4.2.5.1.0.2.1 - Part 1 - Goodfellow,Abadie,Mirza,Xu,Farley,Ozair†,Courville,Bengio - Generative Adversarial Nets
Section 4.2.5.1.0.2.2 - Part 2 - Ali - Pros and Cons of GAN Evaluation Measures
Section 4.2.5.1.0.2.3 - Part 3 - Barrat,Sharma - Note on the Inception Score
Section 4.2.5.1.1 - Deep Convolutional GANs (DCGANs)
Section 4.2.5.1.2 - Conditional GANs (cGANs)
Section 4.2.5.1.2.0 - Detailed Writeup - Miraz,Osindero - Conditional GANs (cGANs)
Section 4.2.5.1.2.1 - InfoGAN
Section 4.2.5.1.2.2 - Auxillary classifier GAN
Section 4.2.5.1.2.3 - Semi-Supervised GAN
Section 4.2.5.1.3 - StackGAN
Section 4.2.5.1.4 - GAN Cost Functions and Loss
Section 4.2.5.1.4.1 - Wasserstein GANs(WGAN)
Section 4.2.5.1.4.2 - Least Squares GAN(LSGAN)
Section 4.2.5.1.4.3 - Energy Based GAN (EBGAN)
Section 4.2.5.1.4.4 - Boundary Equilibrium GAN(BEGAN)
Section 4.2.5.1.4.5 - Boundary Equilibrium GAN(BEGAN)
Section 4.2.5.1.4.6 - Relativistic GAN(R-Standard GAN, R-Average GAN)
Section 4.2.5.1.5 - Image Translation and Advanced GANs
Section 4.2.5.1.5.1 - Px2Pix GAN
Section 4.2.5.1.5.2 - CycleGAN
Section 4.2.5.1.5.3 - BigGAN
Section 4.2.5.1.5.4 - Progessively Growing GAN
Section 4.2.5.1.5.5 - StyleGAN
Section 4.2.6 - Deep Cognitive Models
Section 4.2.6.1 - Hierarchical Temporal Memory
Section 4.2.6.1.0 - Introductory Writeups
Section 4.2.6.1.0.1 - Basic Writeup - Wikipedia - Hierarchical Temporal Memory
Section 4.2.6.1.0.2 - Detailed Writeups
Section 4.2.6.1.0.2.1 - Part 1 - Hawkings,George - Hierarchical Temporal Memory
Section 4.2.6.1.0.2.2 - Part 2 - Mnatzaganian,Kudithipudi,Fokoue - A Mathematical Formalization of Hierarchical Temporal Memory’s Spatial Pooler
Section 4.2.6.2 - Hebbian
Section 4.2.6.2.0 - Basic Writeup - Wikipedia - Hebbian Theory
Section 4.2.6.2.1 - Hebbian Plasticity
Section 4.2.6.2.2 - Hebbian Learning
Section 4.2.6.2.3 - Spike Time Dependent Plasticity
Section 4.2.6.2.3.0 - Introductory Writeups
Section 4.2.6.2.3.0.1 - Basic Writeups
Section 4.2.6.2.3.0.1.1 - Part 1 - Wikipedia - Spike Timing Dependent Plasticity
Section 4.2.6.2.3.0.1.2 - Part 2 - Wikipedia - Spiking Neural Network
Section 4.2.6.2.3.0.1.3 - Part 3 - Pfieffer,Pfiel - Deep Learning With Spiking Neurons: Opportunities and Challenges
Section 4.2.6.2.3.0.2 - Detailed Writeups
Section 4.2.6.2.3.0.2.1 - Part 1 - A Slide Deck
Section 4.2.6.2.3.0.2.2 - Part 2 - Tavanaei,Ghodrati,Kheradpisheh,Masquelier,Maida - Deep Learning in Spike Neural Networks
Section 4.2.6.2.3.0.2.3 - Part 3 - Ponulak,Kasiński - Introduction to spiking neural networks: Information processing, learning and applications
Section 4.2.6.2.3.0.2.4 - Part 4 - Vreeken - Spriking Neural Networks, an introduction
Section 4.2.6.2.4 - Oja
Section 4.2.6.2.5 - Sanger or Generalized Hebbian
Section 4.2.6.2.6 - Generalized Differential Hebbian
Section 4.2.6.2.7 - BCM Theory
Section 4.2.7 - Autoencoders
Section 4.2.7.0 - Basic Writeup - Wikipedia - Autoencoder
Section 4.2.7.1 - Denoising autoencoder
Section 4.2.7.2 - Sparse autoencoder
Section 4.2.7.3 - Variational autoencoder (VAE)
Section 4.2.7.4 - Contractive autoencoder (CAE)
Section 4.2.7.5 - Bidirectional LSTM autoencoder
Section 4.2.7.6 - Bidirectional Encoder Representations from Transformer Attention Networks (BERT)
Section 4.2.8 - Autoassociators
Section 4.2.8.0 - Basic Writeup - Univ of Minnesota - Introduction to Neural Networks: Heteroassociation and Autoassociation
Section 4.2.8.1 - Generalized autoassociator
Section 4.2.8.2 - BSB: Brain State in a Box
Section 4.2.8.3 - Hopfield
Section 4.2.9 - Co-operative Neural Networks
Section 4.2.9.0 - Basic Writeup - Shrivastava,bart,Price,Dai,Dai,Aluru - Cooperative Neural Networks (CoNN)
Section 4.2.9.1 - Cooperative Evolution of Neural Ensembles
Section 4.2.10 - Siamese Neural Networks
Section 4.2.10.0 - Introductory Writeups
Section 4.2.10.0.1 - Basic Writeup - Wikipedia - Siamese Network
Section 4.2.10.0.2 - Detailed Writeup - Chopra,Hadsell,LeCun - Learning a Similarity Metric Discriminatively, with Application to Face Verification
Section 4.2.11 - Triplet Neural Networks
Section 4.2.12 - Activation Functions
Section 4.2.12.0 - Basic Writeup - Wikipedia - Activation Functions
Section 4.2.12.1 - Ridge Functions
Section 4.2.12.1.0 - Basic Writeup - Wikipedia - Ridge Functions
Section 4.2.12.1.1 - Step Functions
Section 4.2.12.1.1.1 - Heaveside (Binary) Functions
Section 4.2.12.1.2 - Linear Functions
Section 4.2.12.1.2.1 - Linear
Section 4.2.12.1.2.2 - Identity
Section 4.2.12.1.3 - Partly Linear Functions
Section 4.2.12.1.3.1 - Rectifier Based Linear Functions
Section 4.2.12.1.3.1.1 - Rectifier
Section 4.2.12.1.3.1.2 - Leaky Rectifier Based
Section 4.2.12.1.3.1.2.1 - Leaky Rectifier
Section 4.2.12.1.3.1.2.2 - Parametric Leaky Rectifier
Section 4.2.12.1.3.1.2.3 - Randomized Leaky Rectifier
Section 4.2.12.1.3.1.3 - Gaussian Error Rectifier
Section 4.2.12.1.3.1.4 - Sigmoid Rectifier
Section 4.2.12.1.3.1.5 - SoftPlus
Section 4.2.12.1.3.1.6 - Exponential Based
Section 4.2.12.1.3.1.6.1 - Exponential
Section 4.2.12.1.3.1.6.2 - Scaled Exponential
Section 4.2.12.1.3.2 - Softshrink
Section 4.2.12.1.3.3 - Adaptive Piecewise Linear
Section 4.2.12.1.3.4 - Long Short-Term Memory Unit Based
Section 4.2.12.1.3.5 - Bent Identity
Section 4.2.12.1.4 - Non-Linear Functions
Section 4.2.12.1.4.1 - Sigmoid Functions
Section 4.2.12.1.4.1.0 - Basic Writeup - Wikipedia - Sigmoid Functions
Section 4.2.12.1.4.1.1 - Logistic
Section 4.2.12.1.4.1.2 - Generalized Logistic
Section 4.2.12.1.4.1.3 - Gudermannian
Section 4.2.12.1.4.1.4 - Hyperbolic Tangent Based
Section 4.2.12.1.4.1.4.1 - TanH
Section 4.2.12.1.4.1.4.2 - Hard TanH
Section 4.2.12.1.4.1.4.3 - ArcTan
Section 4.2.12.1.4.1.5 - Error
Section 4.2.12.1.4.1.6 - SmoothStep
Section 4.2.12.1.4.1.7 - Softsign
Section 4.2.12.1.4.1.8 - Swish
Section 4.2.12.1.4.2 - Algebraic Functions
Section 4.2.12.1.4.2.1 - Square Non-Linearity
Section 4.2.12.1.4.3 - Exponential Functions
Section 4.2.12.1.4.3.1 - Soft Exponential
Section 4.2.12.1.4.3.2 - Softmax
Section 4.2.12.1.4.1 - Circular Functions
Section 4.2.12.1.4.1.1 - SinC
Section 4.2.12.2 - Radial Functions
Section 4.2.12.2.0 - Basic Writeup - Wikipedia - Radial Functions
Section 4.2.12.2.1 - Infinitely Smooth
Section 4.2.12.2.1.1 - Gaussian
Section 4.2.12.2.1.2 - Inverse Multi-Quadratic
Section 4.2.12.2.1.3 - Multi-Quadratic
Section 4.2.12.2.2 - Poly-harmonic Spline
Section 4.2.12.2.3 - Thin Plate Spline
Section 4.2.12.2.4 - Compactly Supported RBFs
Section 4.2.12.2.4.1 - Bump Function
Section 4.2.12.3 - Multi-Fold Functions
Section 4.2.12.3.0 - Basic Writeup - Wikipedia - Multi-Fold Functions
Section 4.2.12.3.1 - Linear Folds
Section 4.2.12.3.2 - Tree-Like Folds
Section 4.2.12.3.3 - Maxout
Section 4.3 - Bayesian Networks - Derived From Probability Theory and Statistics
Section 4.3.0 - Basic Writeup - Wikipedia - Bayesian Networks
Section 4.3.1 - Bayes Theorem
Section 4.3.2 - Chain Rule
Section 4.3.3 - Naive Bayes
Section 4.3.3.0 - Basic Writeup - Wikipedia - Naive Bayes Classifier
Section 4.3.3.1 - Gaussian Naive Bayes
Section 4.3.3.2 - Multinomial Naive Bayes
Section 4.3.3.3 - Bernoulli naive Bayes
Section 4.3.4 - Markov random field
Section 4.3.4.0 - Introductory Writeups
Section 4.3.4.0.1 - Basic Writeup - Wikipedia - Markov Random Field
Section 4.3.4.0.2 - Detailed Writeup - Blake,Kohli - Introduction to Markov Random Fields
Section 4.3.4.1 - Conditional Random Field
Section 4.3.4.1.0 - Introductory Writeups
Section 4.3.4.1.0.1 - Basic Writeup - Wikipedia - Conditional Random Field
Section 4.3.4.1.0.2 - Detailed Writeup - Sutton,McCallum - An Introduction to Conditional Random Fields
Section 4.3.4.1.1 - High Order CRF
Section 4.3.4.1.2 - Variable Order CRF
Section 4.3.4.1.3 - Semi Markov CRF
Section 4.3.4.1.4 - Latent-dynamic CRF
Section 4.3.5 - Markov Models
Section 4.3.5.0 - Basic Writeup - Wikipedia - Markov Model
Section 4.3.5.1 - Hidden Markov Model
Section 4.3.5.1.0 - Introductory Writeups
Section 4.3.5.1.0.1 - Basic Writeup - Hidden Markov Model
Section 4.3.5.1.0.2 - Detailed Writeup - Fine,Singer,Tishby - The Hierarchical Hidden Markov Model: Analysis and Applications
Section 4.3.5.1.1 - Layered Hidden Markov Model
Section 4.3.5.1.2 - Parameterized-HMM (PHMM)
Section 4.3.5.1.3 - Entropic-HMM
Section 4.3.5.1.4 - Variable-length HMM (VHMM)
Section 4.3.5.1.5 - Coupled-HMM (CHMM)
Section 4.3.6 - Bayesian knowledge base
Section 4.4 - Probabilistic Graphical Modeling (PGM) - Derived From Graph Theory, Probability and Statistics
Section 4.4.0 - Introductory Writeups
Section 4.4.0.1 - Basic Writeups
Section 4.4.0.1.1 - Part 1 - Wikipedia - Graphical Model
Section 4.4.0.1.2 - Part 2 - Francis Teng - Probablistic Graphical Models
Section 4.4.0.2 - Detailed Writeups
Section 4.4.1.0.2.1 - Part 1 - Koller,Friedman,Getoor,Taskar - Graphical Models in a Nutshell
Section 4.4.1.0.2.2 - Part 2 - Jordan - An Introduction to Probabilistic Graphical Models
Section 4.4.1 - Basic Graph Theory
Section 4.4.2 - Bayesian networks and Markov random fields
Section 4.4.3 - Naive Bayes Classifier
Section 4.4.4 - Tree Augmented Classifier
Section 4.4.5 - Factor graph
Section 4.4.6 - Clique tree or junction tree
Section 4.4.7 - Chain graph
Section 4.4.8 - Ancestral graph
Section 4.4.9 - Markov random field
Section 4.4.10 - Conditional random field (See Section 4.3.4.2)
Section 4.4.11 - Restricted Boltzmann Machine
Section 4.5 - Ensemble Learning - Derived From Statistics
Section 4.5.0 - Basic Writeups
Section 4.5.0.1 - Part 1 - Wikipedia - Ensemble Learning
Section 4.5.0.2 - Part 2 - Becoming Human.ai - Ensemble Learning: Bagging and Boosting
Section 4.5.1 - Bootstrapping (Statistics)
Section 4.5.2 - Bootstrap aggregating(Bagging)
Section 4.5.2.0 - Introductory Writeups
Section 4.5.2.0.1 - Basic Writeup - Wikipedia - Bootstrap Aggregating (Bagging)
Section 4.5.2.0.2 - Detailed Writeup - Buhlmann,Yu - Explaining Bagging
Section 4.5.2.1 - Bagging Applications
Section 4.5.2.1.1 - Random Forest (Random Decision Forest)
Section 4.5.2.1.2 - Random Subspace Method (Attribute or Feature Bagging)
Section 4.5.3 - Boosting(meta-algorithm)
Section 4.5.3.0 - Basic Writeup - Wikipedia - Boosting
Section 4.5.3.1 - Adaptive Boost (AdaBoost)
Section 4.5.3.1.0 - Basic Writeup - Wikipedia - AdaBoost
Section 4.5.3.1.1 - LogitBoost
Section 4.5.3.2 - Gradient Boost
Section 4.5.3.2.0 - Basic Writeups
Section 4.5.3.2.0.1 - Part 1 - Wikipedia - Gradient Boosting
Section 4.5.3.2.0.2 - Part 2 - Jason Brownlee - A gentle introduction to the Gradient Boosting Algorithm for ML
Section 4.5.3.2.0.3 - Part 3 - Leo Breiman - Prediction Games and Arcing(Adaptive Reweighting and Combining) Algorithms - A statistical framework
Section 4.5.3.2.1 - XGBoost
Section 4.5.3.2.2 - Accelerated Gradient Boosting
Section 4.5.3.2.3 - Stochastic Gradient Boosting
Section 4.5.3.3 - Linear Programming Boost (LPBoost)
Section 4.5.3.4 - Total Boost
Section 4.5.3.5 - Brown Boost
Section 4.5.3.6 - MadaBoost
Section 4.5.3.7 - CoBoosting
Section 4.5.3.8 - RankBoost
Section 4.5.3.9 - Boosting Applications
Section 4.5.3.9.1 - Gradient Boosted Trees
Section 4.5.3.9.1.0 - Basic Writeup - Tianqi Chen - Introduction to Boosted Trees
Section 4.5.3.9.1.1 - Gradient Boosted Decision Trees
Section 4.5.3.9.1.1.0 - Basic Writeup - Flynn Wang - An Introduction to Gradient Boosted Decision Trees and XGBoost
Section 4.5.3.9.1.1.1 - Multi-layered Gradient Boosted Decision Tree
Section 4.5.3.9.1.2 - Gradient Boosted Regression Trees
Section 4.5.3.9.2 - Gradient Boosted Machine
Section 4.5.4 - Bayes optimal classifier
Section 4.5.5 - Bayesian parameter averaging
Section 4.5.6 - Bayesian model(classifier) combination
Section 4.5.7 - Bucket of models
Section 4.5.8 - Stacking
Section 4.6 - Embeddings (Scalar and Graph) - Derived From Structural Math
Section 4.6.0 - Basic Writeup - Wikipedia - Embedding
Section 4.6.1 - Graph Embeddings
Section 4.6.1.0 - Introductory Writeups
Section 4.6.1.0.1 - Basic Writeup - Primoz Godec - Graph Embeddings: The Summary
Section 4.6.1.0.2 - Detailed Writeup - Cai,Zheng,Chang - A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Section 4.6.2 - Word Embedding
Section 4.6.2.0 - Introductory Writeups
Section 4.6.2.0.1 - Basic Writeup- Analytics Vidhya - An Intuitive Understanding of Word Embeddings
Section 4.6.2.0.2 - Detailed Writeup - Yin,Shen - On the Dimensionality of Word Embedding
Section 4.6.2.1 - Word Embeddings in NLP
Section 4.6.2.2 - From Word Embeddings in Sentence Meanings
Section 4.6.2.3 - Introducton to Word Embeddings from a language standpoint
Section 4.6.3 - Neural Network Embeddng
Section 4.6.3.0 - Basic Writeup - Will Koehrsen - Neural Network Embeddings Explained
Section 4.6.3.1 - Structural Deep Network Embedding
Section 4.6.3.2 - Heterogeneous Network Embedding via Deep Architectures
Section 4.6.4 - Visual Embedding
Section 4.6.4.0 - Introductory Writeups
Section 4.6.4.0.1 - Basic Writeup - Çağatay Demiralp - Visual Embedding
Section 4.6.4.0.2 - Detailed Writeup - Demiralp,Scheidegger,Kindlmann,Laidlaw,Heer - Visual Embedding: A Model for Visualization
Section 4.6.4.1 - Deep Visual Embedding
Section 4.6.6 - Feature Set Embedding
Section 4.6.7 - Tree Embeddings
Section 4.6.7.1 - A Basic Primer
Section 4.6.7.2 - Hyperbolic Enbeddings
Section 4.6.7.3 - Poincaré Embeddings
Section 4.6.8 - Context Embeddings
Section 4.7 - Decision Trees - Derived From Graph Theory
Section 4.7.1.0 - Basic Writeup - Wikipedia - Decision Tree Learning
Section 4.7.1 - Classification and regression tree (CART)
Section 4.7.2 - Iterative Dichotomiser 3 (ID3)
Section 4.7.3 - C4.5 algorithm
Section 4.7.4 - C5.0 algorithm
Section 4.7.5 - Chi-squared Automatic Interaction Detection (CHAID)
Section 4.7.6 - Decision stump
Section 4.7.7 - Conditional inference tree
Section 4.7.8 - Random Decision Forest
Section 4.7.9 - Decision Jungle
Section 4.8 - Anomaly Detection - Derived From Data Science
Section 4.8.0 - Introductory Writeups
Section 4.8.0.1 - Basic Writeup - Chandola,Banarjee,Kumar - Anomaly Detection: A Survey
Section 4.8.0.2 - Detailed Writeup - Goldstein,Uchida - A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Section 4.8.1 - Unsupervised Anomaly Detection
Section 4.8.1.0 - Basic Writeup - Chandola,Banarjee,Kumar - Anomaly Detection: A Survey
Section 4.8.1.1 - Nearest Neighbor (Proximity) Based
Section 4.8.1.1.1 - Global Detection
Section 4.8.1.1.1.1 - k-NN Global Anomaly Detection
Section 4.8.1.1.2 - Local Detection
Section 4.8.1.1.2.1 - Density Based Approaches
Section 4.8.1.1.2.1.1 - Local Outlier Factor (LOF)
Section 4.8.1.1.2.1.2 - Connectivity-Based Outlier Factor (COF)
Section 4.8.1.1.2.1.3 - Influenced Outlierness (INFLO)
Section 4.8.1.1.2.1.4 - Local Outlier Probability (LoOP)
Section 4.8.1.1.2.1.5 - Local Correlation Integral (LOCI)
Section 4.8.1.1.2.1.5.0 - Basic Writeup - papadimitrious,Kitagawa,Gibbons,Faloutsos - LOCI: Fast Outlier Detection Using Local Correlation Integral
Section 4.8.1.1.2.1.5.1 - Approximate Local Correlation Integral (aLOCI)
Section 4.8.1.1.2.2 - Distance Based Approaches
Section 4.8.1.1.2.2.0 - Basic Writeup - Kriegel,Kroger,Zimek - Outlier Detection Techniques
Section 4.8.1.1.2.2.1 - Index Based
Section 4.8.1.1.2.2.2 - Nested Loop Based
Section 4.8.1.1.2.2.3 - Grid Based
Section 4.8.1.1.2.3 - High Dimensional Approaches
Section 4.8.1.1.2.3.1 - Angle Based Outlier Degree (ABOD)
Section 4.8.1.1.2.3.2 - Grid-based subspace outlier detection
Section 4.8.1.1.2.3.3 - Subspace Outlier Detection (SOD)
Section 4.8.1.1.2.4 - Depth Based Approaches
Section 4.8.1.1.2.4.1 - ISODEPTH
Section 4.8.1.1.2.4.2 - Fast Depth Contours (FDC)
Section 4.8.1.1.2.5 - Deviation Based Approaches
Section 4.8.1.2 - Cluster Based
Section 4.8.1.2.1 - Global Detection
Section 4.8.1.2.1.1 - Cluster-Based Local Outlier Factor (CBLOF/ uCBLOF)
Section 4.8.1.2.2 - Local Detection
Section 4.8.1.2.2.1 - Local Density Cluster-based Outlier Factor (LDCOF)
Section 4.8.1.2.2.2 - Clustering-based Multivariate Gaussian Outlier Score (CMGOS)
Section 4.8.1.2.2.3 - K-Means Clustering Based
Section 4.8.1.2.2.4 - Fuzzy C-Means (FCM) Based
Section 4.8.1.2.2.5 - Unsupervised Niche Clustering
Section 4.8.1.2.2.6 - Expectation-Maximization Meta Algorithm
Section 4.8.1.3 - Statistical Based
Section 4.8.1.3.1 - Histogram-based Outlier Score (HBOS)
Section 4.8.1.4 - Subspace Based
Section 4.8.1.4.1 - Robust Principal Component Analysis (rPCA)
Section 4.8.1.4.2 - Clustering-based Multivariate Gaussian Outlier Score (CMGOS)
Section 4.8.1.4.3 - Subspace Outlier Detection (SOD)
Section 4.8.1.5 - Classifier Based
Section 4.8.1.5.1 - One-Class Support Vector Machine
Section 4.8.1.6 - Neural Network Based
Section 4.8.1.6.1 - Self Organizing Map (SOM) Based
Section 4.8.1.6.1.0 - Basic Writeup - Stefanovic,Kurosava - Outlier Detection In Self-Organizing Maps and Their Quality Estimation
Section 4.8.1.6.1.1 - Self Organizing Map (SOM) using Particle Swarm Optimization
Section 4.8.1.6.2 - Adaptive Resonance Theory Based
Section 4.8.1.7 - Rank Based
Section 4.8.1.7.0 - Basic Writeup - Patel,Shah - A Survey of Anomalies Detection Using Density Based-Rank Based Outlier Detection Methods
Section 4.8.1.7.1 - Rank Based Detection Algorithm (RBDA)
Section 4.8.1.7.2 - Rank with Average Distance Algorithm (RADA)
Section 4.8.1.7.3 - Outlier Detection Using Modified Ranks (ODMR)
Section 4.8.2 - Supervised Anomaly Detection
Section 4.8.2.1 - K -Nearest Neighbor (k-NN)
Section 4.8.2.2 - Bayesian Network (BN)
Section 4.8.2.3 - Supervised Neural Networks
Section 4.8.2.4 - Decision Tree
Section 4.8.2.5 - Support Vector Machine (SVM)
Section 4.8.3 - Semi-Supervised Anomaly Detection
Section 4.9 - Reinforcement Learning - Derived From Behavorial Psychology
Section 4.9.0 - Introductory Writeups
Section 4.9.0.1 - Basic Writeup - Wikipedia - Reinforcement Learning
Section 4.9.0.2 - Detailed Writeup - Textbook - Sutton,Barto - Reinforcement Learning: An Introduction
Section 4.9.1 - Temporal difference learning
Section 4.9.1.0 - Introductory Writeups
Section 4.9.1.0.1 - Basic Writeup - Wikipedia - Temporal Difference Learning
Section 4.9.1.0.2 - Detailed Writeup - Kunz - An Introduction to Temporal Difference Learning
Section 4.9.2 - Q-learning
Section 4.9.2.0 - Introductory Writeups
Section 4.9.2.0.1 - Basic Writeup - Wikipedia - Q-Learning
Section 4.9.2.0.2 - Detailed Writeup - Watkins,Dayan - Q-Learning
Section 4.9.3 - State Action Reward State Action (SARSA)
Section 4.9.3.0 - Introductory Writeups
Section 4.9.3.0.1 - Basic Writeup - Wikipedia - State Action Reward State Action
Section 4.9.3.0.2 - Detailed Writeups
Section 4.9.3.0.2.1 - Part 1 - Rummery,Niranjan - On-Line Q-Learning Using Connectionist Systems
Section 4.9.3.0.2.2 - Part 2 - Seijen,Hasselt,Whiteson,Wiering - A Theoretical and Empiral Analysis of Expected Sarsa
Section 4.9.4 - Fictitious play
Section 4.9.4.0 - Introductory Writeups
Section 4.9.4.0.1 - Basic Writeup - Wikipedia - Fictitious Play
Section 4.9.4.0.2 - Detailed Writeup - Daskalakis - Topics in Algorithmic Game Theory
Section 4.9.5 - Learning classifier system
Section 4.9.5.0 - Introductory Writeups
Section 4.9.5.0.1 - Basic Writeup - Wikipedia - Learning Classifier System
Section 4.9.5.0.2 - Detailed Writeup - Urbanowicz,Moore - Learning Classifier Systems: A Complete Introduction, Review, and Roadmap
Section 4.9.6 - Optimal control
Section 4.9.6.0 - Introductory Writeups
Section 4.9.6.0.1 - Basic Writeup - Wikipedia - Optimal Control
Section 4.9.6.0.2 - Detailed Writeup - Bertsekas - Reinforcement Learning and Optimal Control
Section 4.9.7 - Error-driven learning
Section 4.9.8 - Multi-agent system
Section 4.9.8.0 - Introductory Writeups
Section 4.9.8.0.1 - Basic Writeup - Wikipedia - Multi-Agent System
Section 4.9.8.0.2 - Detailed Writeups
Section 4.9.8.0.2.1 - Part 1 - Balaji,Srinivasan - An Introduction to Multi-Agent Systems
Section 4.9.8.0.2.2 - Part 2 - Hoek,Wooldridge - Multi-Agent Systems
Section 4.9.9 - Distributed artificial intelligence
Section 4.9.9.0 - Introductory Writeups
Section 4.9.9.0.1 - Basic Writeup - Wikipedia - Distributed Artificial Intelligence
Section 4.9.9.0.2 - Detailed Writeups
Section 4.9.9.0.2.1 - Part 1 - Durfee - Distributed Artificial Intelligence
Section 4.9.9.0.2.2 - Part 2 - Bond,Gasser - A Survey of Distributed Artificial Intelligence
Section 4.9.9.1 - Trends in Distributed AI
Section 4.9.10 - Learning Automata
Section 4.9.10.0 - Introductory Writeups
Section 4.9.10.0.1 - Basic Writeup - Wikipedia - Learning Automata
Section 4.9.10.0.2 - Detailed Writeups
Section 4.9.10.0.2.1 - Part 1 - Narendra,Thathachar - Learning Automata: A Survey
Section 4.9.10.0.2.2 - Part 2 - Narendra,Thathachar - Learning Automata: An Introduction
Section 4.9.11 - Deep Reinforcement Learning
Section 4.9.11.0 - Basic Writeup - Lavet,Islam,Henderson,Bellemare,Pineau - An Introduction to Deep Refinforcement Learning
Section 4.9.11.1 - Deep Q-Network
Section 4.9.11.2 - Deep Deterministic Policy Gradient
Section 4.10 - Reasoning Systems - Derived From Behavorial Psychology
Section 4.10.0 - Introductory Writeups
Section 4.10.0.1 - Basic Writeup - Wikipedia - Reasoning System
Section 4.10.0.2 - Detailed Writeup - Richter,Weber - Case-based reasoning: a textbook
Section 4.10.1 - Constraint Programming (Solvers)
Section 4.10.1.0 - Basic Writeup - Wikipedia - Constraint Programming
Section 4.10.1.1 - Constraint Logic Programming
Section 4.10.1.1.0 - Basic Writeup - Wikipedia - Constraint Logic Programming
Section 4.10.1.1.1 - Concurrent constraint logic programming
Section 4.10.2 - Theorem provers
Section 4.10.2.0 - Basic Writeup - Wikipedia - Automated Theorem Proving
Section 4.10.2.1 - First-order resolution with unification
Section 4.10.2.2 - Model elimination
Section 4.10.2.3 - Method of analytic tableaux
Section 4.10.2.4 - Superposition
Section 4.10.2.5 - Model checking
Section 4.10.2.6 - Mathematical induction
Section 4.10.2.7 - Binary decision diagrams
Section 4.10.2.8 - DPLL
Section 4.10.2.9 - Unification
Section 4.10.2.10 - Rewriting
Section 4.10.3 - Logic programming
Section 4.10.3.0 - Basic Writeup - Wikipedia - Logic Programming
Section 4.10.3.1 - Prolog
Section 4.10.3.2 - Abductive logic programming
Section 4.10.3.3 - Metalogic programming
Section 4.10.3.4 - Constraint logic programming
Section 4.10.3.5 - Concurrent logic programming
Section 4.10.3.6 - Concurrent constraint logic programming
Section 4.10.3.7 - Inductive logic programming
Section 4.10.3.8 - Higher-order logic programming
Section 4.10.3.9 - Linear logic programming
Section 4.10.3.10 - Object-oriented logic programming
Section 4.10.3.11 - Transaction logic programming
Section 4.10.3.12 - Fuzzy logic programming
Section 4.10.3.13 - Fuzzy logic programming
Section 4.10.3.14 - Functional logic programming
Section 4.10.4 - Rule engines
Section 4.10.4.1 - Business Rule engines
Section 4.10.4.2 - Rule Based Machine Learning
Section 4.10.4.2.0 - Basic Writeup - Wikipedia - Rule Based Machine Learning
Section 4.10.4.2.1 - Learning Classifier Systems
Section 4.10.4.2.2 - Association Rule Learning
Section 4.10.4.2.2.0 - Basic Writeup - Wikipedia - Association Rule Learning
Section 4.10.4.2.2.1 - Apriori Algorithm
Section 4.10.4.2.2.2 - Eclat Algorithm
Section 4.10.4.2.2.3 - Frequent Pattern(FP) Growth Algorithm
Section 4.10.4.2.2.4 - AprioriDP Algorithm
Section 4.10.4.2.2.5 - Context Based Association Rule Mining Algorithm
Section 4.10.4.2.2.6 - Node-set-based algorithms
Section 4.10.4.2.2.6.1 - Fast Mining Frequent Itemsets using Nodesets (FIN)
Section 4.10.4.2.2.6.2 - PrePost
Section 4.10.4.2.2.6.3 - PPV
Section 4.10.4.2.2.7 - GUHA procedure ASSOC
Section 4.10.4.2.2.8 - OPUS Search
Section 4.10.4.2.2.9 - Multi-Relation Association Rules
Section 4.10.4.2.2.10 - Context Based Association Rules
Section 4.10.4.2.2.11 - Contrast set learning
Section 4.10.4.2.2.12 - Weighted class learning
Section 4.10.4.2.2.13 - High-order pattern discovery
Section 4.10.4.2.2.14 - K-Optimal pattern discovery
Section 4.10.4.2.2.15 - Approximate Frequent Itemset
Section 4.10.4.2.2.16 - Generalized Association Rules
Section 4.10.4.2.2.17 - Quantitative Association Rules
Section 4.10.4.2.2.18 - Interval Data Association Rules
Section 4.10.4.2.2.19 - Sequential pattern mining
Section 4.10.4.2.2.20 - Subspace Clustering
Section 4.10.4.2.2.21 - Warmr
Section 4.10.4.2.3 - Artificial Immune Systems
Section 4.10.5 - Deductive classifiers
Section 4.10.6 - Machine Learning Systems
Section 4.10.7 - Procedural Reasoning Systems
Section 4.10.8 - Case Based Reasoning Systems
Section 4.11 - Nature Inspired Computation and ML Applications - Derived From Natural Science
Section 4.11.0 - Basic Writeup - Wikipedia - Natural Computing: Nature Inspired Models of Computation
Section 4.11.1 - Evolutionary Computation
Section 4.11.1.0 - Basic Writeup - Wikipedia - Evolutionary Computation
Section 4.11.1.1 - Evolutionary Algorithms and Techniques
Section 4.11.1.1.0 - Introductory Writeups
Section 4.11.1.1.0.1 - Basic Writeup - Wikipedia - Evolutionary Algorithm
Section 4.11.1.1.0.2 - Detailed Writeups
Section 4.11.1.1.0.2.1 - Part 1 - Streichert - Introduction to Evolutionary Algorithms
Section 4.11.1.1.0.2.2 - Part 2 - Texbook - Pohlheim - Evolutionary Algorithms: Overview, Methods and Operators
Section 4.11.1.1.0.2.3 - Part 3 - Mühlenbein - Evolutionary Algorithms: Theory and Applications
Section 4.11.1.1.1 - Cellular evolutionary algorithm
Section 4.11.1.1.2 - Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Section 4.11.1.1.3 - Differential evolution
Section 4.11.1.1.4 - Evolutionary programming
Section 4.11.1.1.5 - Artificial development
Section 4.11.1.1.6 - Cultural algorithm
Section 4.11.1.1.7 - Differential evolution
Section 4.11.1.1.8 - Effective fitness
Section 4.11.1.1.9 - Evolution strategy
Section 4.11.1.1.10 - Gaussian adaptation
Section 4.11.1.1.11 - Evolutionary multimodal optimization
Section 4.11.1.1.12 - Grammatical evolution
Section 4.11.1.1.13 - Particle swarm optimization
Section 4.11.1.1.14 - Memetic algorithm
Section 4.11.1.1.15 - Natural evolution strategy
Section 4.11.1.1.16 - Spiral optimization algorithm
Section 4.11.1.1.17 - Promoter Based Genetic Algorithm
Section 4.11.1.18 - Genetic Algorithm
Section 4.11.1.18.0 - Introductory Writeups
Section 4.11.1.18.0.1 - Basic Writeup - Wikipedia - Genetic Algorithm
Section 4.11.1.18.0.2 - Detailed Writeups
Section 4.11.1.18.0.2.1 - Part 1 - Carr - An Introduction to Genetic Algorithms
Section 4.11.1.18.0.2.2 - Part 2 - Sastry,Goldberg,Kendall - Genetic Algorithms
Section 4.11.1.18.1 - Chromosome
Section 4.11.1.18.2 - Clonal selection algorithm
Section 4.11.1.18.2.0 - Introductory Writeups
Section 4.11.1.18.2.0.1 - Basic Writeup - Wikipedia - Clonal Selection Algorithm
Section 4.11.1.18.2.0.2 - Detailed Writeup - Brownlee - Clonal Selection Algorithm
Section 4.11.1.18.3 - Crossover
Section 4.11.1.18.4 - Mutation
Section 4.11.1.18.5 - Genetic memory
Section 4.11.1.18.6 - Genetic fuzzy systems
Section 4.11.1.18.7 - Selection
Section 4.11.1.18.8 - Fly algorithm
Section 4.11.1.19 - Genetic Programming
Section 4.11.1.19.0 - Basic Writeup - Wikipedia - Genetic Programming
Section 4.11.1.19.1 - Cartesian genetic programming
Section 4.11.1.19.2 - Linear genetic programming
Section 4.11.1.19.3 - Multi expression programming
Section 4.11.1.19.4 - Schema
Section 4.11.1.19.5 - Eurisko
Section 4.11.1.19.6 - Parity benchmark
Section 4.11.1.20 - Gene expression programming
Section 4.11.1.21 - Dual-phase evolution
Section 4.11.1.22 - Evolution Strategy
Section 4.11.1.23 - Neuroevolution
Section 4.11.1.24 - Learning classifier system
Section 4.11.1.25 - Estimation of Distribution Algorithms
Section 4.11.1.26 - Learnable Evolution Model
Section 4.11.1.27 - Synergistic Fibroblast Optimization
Section 4.11.1.28 - Self Organization
Section 4.11.1.28.0 - Basic Writeup - Wikipedia - Self Organization
Section 4.11.1.28.1 - Self Organizing Maps
Section 4.11.1.28.2 - Competitive Learning (See Section 4.2.4 on Competitive Networks)
Section 4.11.1.2 - Related Techniques
Section 4.11.1.2.1 - Swarm intelligence
Section 4.11.1.2.2 - Ant Colony Optimization
Section 4.11.1.2.3 - Bees algorithm
Section 4.11.1.2.3.0 - Basic Writeup - Wikipedia - Bees Algorithm
Section 4.11.1.2.3.1 - Artificial Bee Colony Algorithm
Section 4.11.1.2.4 - Cuckoo search
Section 4.11.1.2.5 - Particle swarm optimization
Section 4.11.1.2.6 - Bacterial Colony Optimization
Section 4.11.1.3 - Metaheuristic Techniques
Section 4.11.1.3.1 - Grey Wolf Optimizer
Section 4.11.1.3.2 - Firefly algorithm
Section 4.11.1.3.3 - Harmony search
Section 4.11.1.3.4 - Gaussian adaptation
Section 4.11.1.3.5 - Memetic algorithm
Section 4.11.2 - Membrane computing
Section 4.11.3 - Neural computation (See Section 4.2 on Artificial Neural Network)
Section 4.11.4 - Swarm Intelligence
Section 4.11.5 - Artificial Immune Computinge
Section 4.11.6 - Cellular automata
Section 4.11.7 - Amorphous computing
Section 4.11.8 - Nature Insipired Novel Hardware
Section 4.11.8.1 - Molecular (DNA) Computing
Section 4.11.8.2 - Peptide Computing
Section 4.11.8.3 - Quantum Computing (See Section 4.19 on Quantum Computing and Quantum Machine Learning)
Section 4.12 - Instance Based and Lazy Learning - Derived From Psychology and Statistics
Section 4.12.0 - Basic Writeups
Section 4.12.0.1 - Part 1 - Wikipedia - Instance Based Learning
Section 4.12.0.2 - Part 2 - Wikipedia - Lazy Learning
Section 4.12.1 - Kernel Machines
Section 4.12.2 - k-nearest neighbor
Section 4.12.3 - RBF Networks
Section 4.13 - Support Vector Machines - Derived From Structural Math
Section 4.13.0 - Introductory Writeups
Section 4.13.0.1 - Basic Writeups
Section 4.13.0.1.1 - Part 1 - Wikipedia - Support Vector Machine
Section 4.13.0.1.2 - Part 2 - Savan Patel - SVM Theory
Section 4.13.0.1.3 - Part 3 - Ajay Yadav - Support Vector Machines
Section 4.13.0.2 - Detailed Writeups
Section 4.13.0.2.1 - Part 1 - Ng - Support Vector Machines
Section 4.13.0.2.2 - Part 2 - Jakkula - Tutorial on Support Vector Machine (SVM)
Section 4.13.0.2.3 - Part 3 - Ben-Hur,Weston - A User’s Guide to Support Vector Machines
Section 4.13.0.2.4 - Part 4 - Berwick - An Idiot’s guide to Support vector machines (SVMs)
Section 4.13.1 - Linear SVM
Section 4.13.1.0 - Basic Writeup - JMLR - Linear SVM
Section 4.13.1.1 - Hard-margin SVM
Section 4.13.1.1.0 - Introductory Writeups
Section 4.13.1.1.0.1 - Basic Writeup - Wikipedia - SVM: Hard Margin
Section 4.13.1.1.0.2 - Detailed Writeup - Boser,Guyon,Vapnik - A Training Algorithm for Optimal Margin Classifiers
Section 4.13.1.2 - Soft-margin SVM
Section 4.13.1.2.0 - Introductory Writeups
Section 4.13.1.2.0.1 - Basic Writeup - Wikipedia - SVM: Soft Margin
Section 4.13.1.2.0.2 - Detailed Writeup - Cortes,Vapnik - Support Vector Networks
Section 4.13.1.3 - Langrangian SVM
Section 4.13.1.3.0 - Introductory Writeups
Section 4.13.1.3.0.1 - Basic Writeup - JMLR - Lagrangian SVM
Section 4.13.1.3.0.2 - Detailed Writeup - Mangasarian,Musicant - Lagrangian Support Vector Machines
Section 4.13.2 - Non-Linear SVM
Section 4.13.2.0 - Introductory Writeups
Section 4.13.2.0.1 - Basic Writeups
Section 4.13.2.0.1.1 - Part 1 - Wikipedia - SVM: NonLinear Classification
Section 4.13.2.0.1.2 - Part 2
Section 4.13.2.0.2 - Detailed Writeup - Rai - Kernel Methods and Nonlinear Classification
Section 4.13.2.1 - Common Kernel Tricks in SVMs(Mappings from Non-Linear to Linear Space)
Section 4.13.2.1.0 - Basic Writeup - Wikipedia - SVM: Kernel Trick
Section 4.13.2.1.1 - Polynomial (homogeneous)
Section 4.13.2.1.2 - Polynomial (inhomogeneous)
Section 4.13.2.1.3 - Gaussian Radial Based Function
Section 4.13.2.1.4 - Hyperbolic Tangent
Section 4.13.2.1.5 - 25 Kernel Tricks (All on one page with code pointers)
Section 4.13.3 - Computing the SVM classifier
Section 4.13.3.0 - Basic Writeup - Wikipedia - SVM: Computing the SVM Classifier
Section 4.13.3.1 - Primal
Section 4.13.3.2 - Dual
Section 4.13.3.3 - Kernel trick
Section 4.13.3.4 - Modern methods
Section 4.13.3.4.0 - Basic Writeup - Wikipedia - SVM: Modern Methods
Section 4.13.3.4.1 - Sub-gradient descent
Section 4.13.3.4.2 - Coordinate descent
Section 4.13.3.5 - Empirical risk minimization
Section 4.13.3.5.0 - Basic Writeup - Wikipedia - Empirical Risk Minimization
Section 4.13.3.5.1 - Risk minimization (See Section 2.7.1.4.1 on Loss Functions for details)
Section 4.13.3.5.2 - Regularization and stability (See Section 4.18 on Regularization)
Section 4.13.3.5.3 - SVM and the hinge loss (See Section 2.7.1.4.1.3.2 Classification of Loss Functions)
Section 4.13.4 - Extensions
Section 4.13.4.1 - Support vector clustering (SVC)
Section 4.13.4.2 - Multiclass SVM
Section 4.13.4.2.0 - Introductory Writeups
Section 4.13.4.2.0.1 - Basic Writeup - Stanford - Multi-Class SVM
Section 4.13.4.2.0.2 - Detailed Writeups
Section 4.13.4.2.0.2.1 - Part 1 - Ahuja,Yadav - Multiclass Classification and Support Vector Machine
Section 4.13.4.2.0.2.2 - Part 2 - Hsu,Lin - A Comparison of Methods for Multi-class Support Vector Machines
Section 4.13.4.2.1 - One-vs-One Classification
Section 4.13.4.2.2 - One-vs-All Classification
Section 4.13.4.2.2.0 - Basic Writeup - Scikit - Multi-Class: One-vs-All Classification
Section 4.13.4.2.2.1 - Multi-Class Classification
Section 4.13.4.2.2.2 - Multilabel Classification
Section 4.13.4.2.3 - Directed Acyclic Graph SVM (DAGSVM)
Section 4.13.4.2.4 - Error Correcting Output Codes
Section 4.13.4.2.4.0 - Basic Writeup - Dietterich,Bakiri - Solving Multiclass Learning Problems via Error-Correcting Output Codes
Section 4.13.4.2.4.1 - Top-K
Section 4.13.4.3 - Transductive support vector machines
Section 4.13.4.4 - Structured SVM
Section 4.13.4.4.0 - Introductory Writeups
Section 4.13.4.4.0.1 - Basic Writeup - Wikipedia - Structured SVM
Section 4.13.4.4.0.2 - Detailed Writeup - Joachims,Hofmann,Yue,Yu - Predicting Structured Objects with Support Vector Machines
Section 4.13.4.5 - SVM Regression
Section 4.13.4.5.1 - Detailed Writeups
Section 4.13.4.5.1.1 - Part 1 - Smola,Sch¨olkopf - A Tutorial on Support Vector Regression
Section 4.13.4.5.1.2 - Part 2 - Drucker,Burges,Kaufman,Smola,Vapnik - Support Vector Regression Machines
Section 4.13.4.6 - Bayesian SVM
Section 4.13.4.6.1 - Detailed Writeups
Section 4.13.4.6.1.1 - Part 1 - Thesis - Wei - Bayesian Approach to Support Vector Machines
Section 4.13.4.6.1.2 - Part 2 - Sollich - Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
Section 4.13.4.7 - SVM Clustering
Section 4.13.4.7.1 - Detailed Writeups
Section 4.13.4.7.1.1 - Part 1 - Ben-Hur,Horn,Siegelmann,Vapnik - Support Vector Clustering
Section 4.13.4.7.1.2 - Part 2 - Hilt,Merat - SVM Clustering
Section 4.13.4.7.1.3 - Part 3 - Finley,Joachims - Supervised Clustering with Support Vector Machines
Section 4.13.4.8 - One-Class SVM for Outlier Detection (See Section 4.8.1.5.1 Unsupervised Anomaly Detection - Classification Based
Section 4.14 - Statisical Classification - Derived From Statistics
Section 4.14.0 - Basic Writeup - Wikipedia - Statistical Classification
Section 4.14.1 - Generative Classification
Section 4.14.1.0 - Basic Writeup - Wikipedia - Generative Model
Section 4.14.1.1 - Hidden Markov model as a HMM Sequence Classifier
Section 4.14.1.2 - Probabilistic context-free grammar as a Classifier
Section 4.14.1.3 - Averaged one-dependence estimators (AODE)
Section 4.14.1.4 - Labelled Latent Dirichlet allocation
Section 4.14.1.5 - Multi-Grain LDA
Section 4.14.1.6 - Restricted Boltzmann machine for Classification
Section 4.14.1.7 - Linear classifiers
Section 4.14.1.7.0 - Basic Writeup - Wikipedia - Linear Classifier
Section 4.14.1.7.1 - Fisher's linear discriminant (Linear Discriminant Analysis - LDA)
Section 4.14.1.7.2 - Naive Bayes Classifer
Section 4.14.1.7.2.0 - Basic Writeups
Section 4.14.1.7.2.0.1 - Part 1 - Wikipedia - Naive Bayes Classifier
Section 4.14.1.7.2.0.2 - Part 2 - Stackexchange - Difference Between Naive Bayes and Multinomial Naive Bayes
Section 4.14.1.7.2.1 - Gaussian naive Bayes
Section 4.14.1.7.2.2 - Multinomial naive Bayes
Section 4.14.1.7.2.3 - Bernoulli naive Bayes
Section 4.14.1.8 - Mixture Models
Section 4.14.1.8.0 - Basic Writeup - Wikipedia - Mixture Model
Section 4.14.1.8.1 - Categorical mixture model
Section 4.14.2 - Discriminative Classification
Section 4.14.2.0 - Basic Writeup - Wikipedia - Discriminative Model
Section 4.14.2.1 - Linear classifiers
Section 4.14.2.1.0 - Basic Writeup - Wikipedia - Linear Classifier
Section 4.14.2.1.1 - Logistic regression
Section 4.14.2.1.2 - Perceptron
Section 4.14.2.1.3 - Support vector machines (See Section 4.13 on SVMs)
Section 4.14.2.1.3.0 - Basic Writeup - Wikipedia - Support Vector Machine
Section 4.14.2.1.3.1 - Linear SVM
Section 4.14.2.1.3.2 - Least squares support vector machines
Section 4.14.2.2 - Quadratic classifiers
Section 4.14.2.3 - Kernel estimation
Section 4.14.2.3.0 - Basic Writeup - Wikipedia - Variable Kernel Density Estimation: Use for Statistical Classification
Section 4.14.2.3.1 - k-nearest neighbor
Section 4.14.2.4 - Boosting Ensemble (meta-algorithm - See Sction 10.5)
Section 4.14.2.5 - Conditional Random Fields (See Section 4.3.4.1)
Section 4.14.2.6 - Decision trees (See Section 4.7)
Section 4.14.2.7 - Random forests (Ensemble - See Section 4.7.8)
Section 4.14.2.8 - Neural networks (See Section 4.2 on ANN's)
Section 4.14.2.9 - Learning vector quantization (See Section 4.2.1.10.1 in Feed Forward Networks)
Section 4.15 - Latent Variable Modeling - Derived From Structural Mathematics
Section 4.15.0 - Basic Writeup - Wikipedia - Latent Variable Model
Section 4.15.1 - Blind signal separation techniques
Section 4.15.1.0 - Basic Writeup - Wikipedia - Blind Signal Separation
Section 4.15.1.1 - Principal components analysis
Section 4.15.1.2 - Singular value decomposition
Section 4.15.1.3 - Independent component analysis
Section 4.15.1.4 - Dependent component analysis
Section 4.15.1.5 - Non-negative matrix factorization
Section 4.15.1.6 - Low-complexity coding and decoding
Section 4.15.1.7 - Stationary subspace analysis
Section 4.15.1.8 - Common spatial pattern
Section 4.15.2 - Expectation–maximization algorithm (EM)
Section 4.15.3 - Method of moments
Section 4.16 - Cluster Analysis - Derived From Structural Mathematics and Statistics
Section 4.16.0 - Basic Writeup - Wikipedia - Cluster Analysis
Section 4.16.1 - Hierarchical or Connectivity Based clustering
Section 4.16.1.0 - Basic Writeup - Wikipedia - Hierarchical Clustering
Section 4.16.1.1 - Hierarchical Agglomerative Clustering
Section 4.16.1.1.0 - Basic Writeup - Wikipedia - Hierarchical Clustering: Agglomerative Clustering Example
Section 4.16.1.1.1 - Single-linkage clustering
Section 4.16.1.1.2 - Complete-linkage Clustering
Section 4.16.1.1.3 - Average Linkage Clustering
Section 4.16.1.1.3.0 - Basic Writeup - Stanford - Average Link Clustering
Section 4.16.1.1.3.1 - Unweighted Pair Group Method with Arithmetic Mean
Section 4.16.1.1.3.2 - Weighted Pair Group Method with Arithmetic Mean
Section 4.16.1.1.4 - Conceptual clustering
Section 4.16.1.1.4.0 - Basic Writeup - Wikipedia - Conceptual Clustering
Section 4.16.1.1.4.1 - CLUSTER/2
Section 4.16.1.1.4.2 - COBWEB
Section 4.16.1.1.4.3 - CYRUS
Section 4.16.1.1.4.4 - GALOIS
Section 4.16.1.1.4.5 - GCF
Section 4.16.1.1.4.6 - INC
Section 4.16.1.1.4.7 - ITERATE
Section 4.16.1.1.4.8 - LABYRINTH
Section 4.16.1.1.4.9 - SUBDUE
Section 4.16.1.1.4.10 - UNIMEM
Section 4.16.1.1.4.11 - WITT
Section 4.16.1.1.5 - Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)
Section 4.16.1.2 - Hierarchical Divisive Clustering
Section 4.16.2 - Centroid-based clustering
Section 4.16.2.0 - Basic Writeup - Uppada - Centroid based Clustering Algorithms - A Clarion Study
Section 4.16.2.1 - Lloyd's Algorithm
Section 4.16.2.2 - K-means
Section 4.16.2.2.0 - Basic Writeup - Wikipedia - K-Means Clustering
Section 4.16.2.2.1 - K-means++
Section 4.16.2.2.2 - Fuzzy C-Means clustering
Section 4.16.2.2.3 - iMWK-Means
Section 4.16.2.2.4 - K-Harmonic Means
Section 4.16.2.3 - K-medians
Section 4.16.2.4 - k-medoids
Section 4.16.2.4.1 - Partitioning Around Mediods(PAM)
Section 4.16.2.4.2 - Clustering Large Applications(CLARA)
Section 4.16.2.4.3 - Clustering Large Applications based on Randomized Search(CLARANS)
Section 4.16.2.4.4 - K-Harmonic Means
Section 4.16.2.4.5 - Generalized K-Harmonic Means
Section 4.16.3 - Distribution-based clustering
Section 4.16.3.1 - Expectation-maximization (EM)
Section 4.16.4 - Density-based clustering
Section 4.16.4.0 - Basic Writeup - Shah,Napanda,D'Mello - Density Based Clustering Algorithms
Section 4.16.4.1 - DBSCAN
Section 4.16.4.2 - OPTICS algorithm
Section 4.16.4.3 - Mean-shift
Section 4.16.5 - Clustering high-dimensional data
Section 4.16.5.0 - Basic Writeup - Wikipedia - Clustering High Dimensional Data
Section 4.16.5.1 - Subspace clustering
Section 4.16.5.1.0 - Basic Writeup - Parsons,Haque,Liu - Subspace Clustering for High Dimensional Data
Section 4.16.5.1.1 - Top Down Search Iterative Methods
Section 4.16.5.1.1.1 - Per Cluster Weightings
Section 4.16.5.1.1.1.1 - Projected Clustering (PROCLUS)
Section 4.16.5.1.1.1.2 - OROCLUS
Section 4.16.5.1.1.1.3 - FINDIT
Section 4.16.5.1.1.1.4 - δ-Clusters
Section 4.16.5.1.1.2 - Per Instance Weightings
Section 4.16.5.1.1.2.1 - COSA
Section 4.16.5.1.1.2.2 - Entropy Based K-Modes (EBK-Modes)
Section 4.16.5.1.1.2.3 - CBK-Modes
Section 4.16.5.1.2 - Bottoms Up Grid Search Based Methods
Section 4.16.5.1.2.1 - Static Grid
Section 4.16.5.1.2.1.1- CLIQUE
Section 4.16.5.1.2.1.2- ENCLUS
Section 4.16.5.1.2.2 - Adaptive Grid
Section 4.16.5.1.2.2.1 - MAFIA
Section 4.16.5.1.2.2.2 - Cell Based Clustering (CBF)
Section 4.16.5.1.2.2.3 - CLTREE
Section 4.16.5.1.2.2.4 - Density-based Optimal projective Clustering (DOC)
Section 4.16.5.1.2.2.5 - SUBCLU
Section 4.16.5.2 - Projected clustering
Section 4.16.5.2.0 - Introductory Writeups
Section 4.16.5.2.0.1 - Basic Writeup - Wikipedia - Clustering High Dimensional Data: Projected Clustering
Section 4.16.5.2.0.2 - Detailed Writeup - Yip,Cheung,Ng - A Review on Projected Clustering Algorithms
Section 4.16.5.2.1 - PreDeCon algorithm
Section 4.16.5.2.2 - CLIQUE
Section 4.16.5.2.3 - ENCLUS
Section 4.16.5.2.4 - MAFIA
Section 4.16.5.2.5 - Clustering Based on Association Rule Hypergraphs
Section 4.16.5.2.6 - Density-based Optimal projective Clustering (DOC)
Section 4.16.5.2.7 - Fast Density-based Optimal projective Clustering (FastDOC)
Section 4.16.5.2.8 - Projected Clustering (PROCLUS)
Section 4.16.5.2.9 - OROCLUS
Section 4.16.5.2.10 - Hierarchical approach with Automatic Relevant dimension selection for Projected clustering (HARP)
Section 4.16.5.3 - Hybrid approaches
Section 4.16.5.3.0 - Basic Writeup - Wikipedia - Clustering High Dimensional Data: Hybrid Approaches
Section 4.16.5.3.1 - Firefly Algorithm For Data Clustering
Section 4.16.5.3.2 - General Hybrid Clustering
Section 4.16.5.3.3 - K-SVMeans
Section 4.16.5.4 - Correlation clustering
Section 4.16.5.4.0 - Introductory Writeups
Section 4.16.5.4.0.1 - Basic Writeup - Wikipedia - Correlation Clustering
Section 4.16.5.4.0.2 - Detailed Writeups
Section 4.16.5.4.0.2.1 - Part 1 - Bansal,Blum,Chawla - Correlation Clustering
Section 4.16.5.4.0.2.2 - Part 2 - Becker - A Survey of Correlation Clustering
Section 4.16.5.4.1 - Approximation algorithms
Section 4.16.5.5 - Stochastic Data clustering
Section 4.16.5.5.0 - Basic Writeup - Meyer,Wesell - Stochastic Data Clustering
Section 4.16.5.5.1 - Approximate Algorithms for Stochastic Clustering
Section 4.17 - Information Filtering - Derived From Information Theory
Section 4.17.0 - Basic Writeup - Wikpedia - Information Filtering System
Section 4.17.1 - Recommender systems
Section 4.17.1.0 - Introductory Writeups
Section 4.17.1.0.1 - Basic Writeup - Wikipedia - Recommender System
Section 4.17.1.0.2 - Detailed Writeups
Section 4.17.1.0.2.1 - Part 1 - Stanford - Recommendation Systems
Section 4.17.1.0.2.2 - Part 2 - Dacrema,Cremonesi,Jannach - Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
Section 4.17.1.0.2.3 - Part 3 - Ebesu,Shen,Fang - Collaborative Memory Networks (CMN)
Section 4.17.1.0.2.4 - Part 4 - Hu,Shi,Zhao,Yu - Metapath based Context for RECommendation (MCRec)
Section 4.17.1.0.2.5 - Part 5 - Li,She - Collaborative Variational Autoencoder (CVAE)
Section 4.17.1.0.2.6 - Part 6 - Wang,Wang,Yeung - Collaborative Deep Learning (CDL)
Section 4.17.1.0.2.7 - Part 7 - He,Liao,Zhang - Neural Collaborative Filtering (NCF)
Section 4.17.1.0.2.8 - Part 8 - Zheng,Lu,Jiang,Zhang,Yu - Spectral Collaborative Filtering (SpectralCF)
Section 4.17.1.0.2.9 - Part 9 - Liang,Krishnan,Hoffman,Jebara - Variational Autoencoders for Collaborative Filtering (Mult-VAE)
Section 4.17.1.1 - Collaborative filtering
Section 4.17.1.1.0 - Introductory Writeups
Section 4.17.1.1.0.1 - Basic Writeup - Wikipedia - Collaborative Filtering
Section 4.17.1.1.0.2 - Detailed Writeup - Nilashi,Bagherifard,Ibrahim,Alizadeh - Collaborative Filter Recommender Systems
Section 4.17.1.1.1 - Memory-based
Section 4.17.1.1.1.0 - Introductory Writeups
Section 4.17.1.1.1.0.1 - Basic Writeup - Wikipedia - Collaborative Filtering: Memory Based
Section 4.17.1.1.1.0.2 - Detailed Writeup - Thesis - Levinas - An Analysis of Memory Based Collaborative Filtering Recommender Systems with Improvement Proposals
Section 4.17.1.1.1.1 - Neighbor Based (Item/User with Pearson/vector cosine Correlation)
Section 4.17.1.1.1.2 - Item/User Based Top-N Recommendation
Section 4.17.1.1.2- Model-based
Section 4.17.1.1.2.0 - Introductory Writeups
Section 4.17.1.1.2.0.1 - Basic Writeup - Wikipedia - Collaborative Filtering: Model Based
Section 4.17.1.1.2.0.2 - Detailed Writeup - Do,Nguyen - Model-based approach for Collaborative Filtering
Section 4.17.1.1.2.1 - Bayesian Belief Nets CF
Section 4.17.1.1.2.2 - Clustering CF
Section 4.17.1.1.2.2.1 - Detailed Writeups
Section 4.17.1.1.2.2.1.1 - Part 1 - Ungar,Foster - Clustering Methods for Collaborative Filtering
Section 4.17.1.1.2.2.1.2 - Part 2 - O’Connor,Herlocker - Clustering Items for Collaborative Filtering
Section 4.17.1.1.2.2.1.3 - Part 3 - George,Merugu - A Scalable Collaborative Filtering Framework based on Co-clustering
Section 4.17.1.1.2.3 - Markov Decision Process Based CF
Section 4.17.1.1.2.4 - Latent Semantic CF
Section 4.17.1.1.2.5 - Sparse Factor Analysis
Section 4.17.1.1.2.6 - Using Dimensionality Reduction (SVD,PVC) CF
Section 4.17.1.1.3 - Hybrid
Section 4.17.1.1.3.0 - Introductory Writeups
Section 4.17.1.1.3.0.1 - Basic Writeup - Wikipedia - Collaborative Filtering: Hybrid
Section 4.17.1.1.3.0.2 - Detailed Writeup - Su,Greiner,Khoshgoftaar,Zhu - Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts
Section 4.17.1.1.3.1 - Content Based CF - FAB
Section 4.17.1.1.3.2 - Content Boosted CF
Section 4.17.1.1.3.3 - Memory-Model Hybrid - Personality Diagnosis CF
Section 4.17.1.2 - Content-based filtering
Section 4.17.1.2.0 - Introductory Writeups
Section 4.17.1.2.0.1 - Basic Writeup - Wikipedia - Recommender System: Content Based Filtering
Section 4.17.1.2.0.2 - Detailed Writeups
Section 4.17.1.2.0.2.1 - Part 1 - Lops,Gemmis,Semeraro - Content-based Recommender Systems: State of the Art and Trends
Section 4.17.1.2.0.2.2 - Part 2 - Zisopoulos,Karagiannidis,Demirtsoglou,Antaris - Content-Based Recommendation Systems
Section 4.17.1.2.0.2.3 - Part 3 - Pazzani,Billsus - Content-Based Recommender Systems
Section 4.17.1.3 - Demographic filtering
Section 4.17.1.4 - Knowledge-Based filtering (Recommender)
Section 4.17.1.4.0 - Introductory Writeups
Section 4.17.1.4.0.1 - Basic Writeup - Wikipedia - Knowledge Based Recommender System
Section 4.17.1.4.0.2 - Detailed Writeup - Burke - Knowledge-based recommender systems
Section 4.17.1.5 - Utility Based Filtering (Recommender)
Section 4.17.1.6 - Multi-criteria recommender systems
Section 4.17.1.6.0 - Introductory Writeups
Section 4.17.1.6.0.1 - Basic Writeup - Wikipedia - Recommender System: Multi-Criteria Recommender Systems
Section 4.17.1.6.0.2 - Detailed Writeups
Section 4.17.1.6.0.2.1 - Part 1 - Hdioud,Frikh,Ouhbi,Khalil - Multi-Criteria Recommender Systems: A Survey and a Method to Learn New User's Profile
Section 4.17.1.6.0.2.2 - Part 2 - Hdioud,Ouhbi,Firkh - Multi-Criteria Recommender Systems based on Multi-Attribute Decision Making
Section 4.17.1.6.0.2.3 - Part 3 - Lakiotaki,Matsatsinis,Tsoukiàs - Multi-Criteria User Modeling in Recommender Systems
Section 4.17.1.6.0.2.4 - Part 4 - Thesis - Rodríguez - Location Aware Multi-Criteria Recommender System for Intelligent Data Mining
Section 4.17.1.7 - Awareness Based Recommender systems
Section 4.17.1.7.1 - Risk-aware recommender systems
Section 4.17.1.7.1.0 - Introductory Writeups
Section 4.17.1.7.1.0.1 - Basic Writeup - Wikipedia - Recommender System: Risk Aware Recommender Systems
Section 4.17.1.7.1.0.2 - Detailed Writeups
Section 4.17.1.7.1.0.2.1 - Part 1 - Bouneffouf,Bouzeghoub,Gançarski - Risk-Aware Recommender Systems
Section 4.17.1.7.1.0.2.2 - Part 2 - Bouneffouf - R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems
Section 4.17.1.7.1.0.2.3 - Part 3 - Bouneffouf - DRARS, A Dynamic Risk-Aware Recommender System
Section 4.17.1.7.2 - Similarity-Aware recommender systems
Section 4.17.1.7.3 - Sequence-Aware recommender systems
Section 4.17.1.7.3.1 - Detailed Writeups
Section 4.17.1.7.3.1.1 - Part 1 - Quadrana,Cremonesi,Jannach - Sequence-Aware Recommender Systems
Section 4.17.1.7.3.1.2 - Part 2 - Thesis - Quadrana - Algorithms for Sequence Aware Recommender Systems
Section 4.17.1.7.4 - Context-Aware recommender systems
Section 4.17.1.7.4.1 - Detailed Writeups
Section 4.17.1.7.4.1.1 - Part 1 - Seyednezhad,Cozart,Bowllan,Smith - A Review on Recommendation Systems: Context-aware to Social-based
Section 4.17.1.7.4.1.2 - Part 2 - Mohamed,Soliman,Sewisy - A Context-Aware Recommender System for Personalized Places in Mobile Applications
Section 4.17.1.7.4.1.3 - Part 3 - Zheng,Buke,Mobasher - Similarity-Based Context-Aware Recommendation
Section 4.17.1.7.4.1.4 - Part 4 - Abbas,Zhang,Khan - A Survey on Context-aware Recommender Systems Based on Computationally Intelligent Techniques
Section 4.17.1.7.5 - Location Aware recommender systems
Section 4.17.1.7.6 - Fairness Aware recommender systems
Section 4.17.1.7.7 - Trust Aware recommender systems
Section 4.17.1.8 - Hybrid recommender systems
Section 4.17.1.8.0 - Introductory Writeups
Section 4.17.1.8.0.1 - Basic Writeup - Wikipedia - Recommender System: Hybrid Recommender Systems
Section 4.17.1.8.0.2 - Detailed Writeup
Section 4.17.2 - Content Discovery Platforms
Section 4.18 - Regularization - Derived From Statistics, Mathematics(Optimization theory) and Computer Science Theory
Section 4.18.0 - Introductory Writeups
Section 4.18.0.1 - Basic Writeup - Wikipedia - Regularizatin (Statistics)
Section 4.18.0.2 - Detailed Writeup - Bauer,Pereverzev,Rosasco - On Regularization Algorithms in Learning Theory
Section 4.18.1 - Bayesian Interpretation of Regularization
Section 4.18.2 - Regularization by Spectral Filtering
Section 4.18.3 - Matrix Regularization
Section 4.18.4 - Elastic Net Regularization
Section 4.18.5 - Total Variation(Total Variation Denoising) Regularization
Section 4.18.6 - Generalization
Section 4.18.7 - Tikhonov (Ridge, Weight Decay, Linear, Constrained Linear Inversion, Tikhonov-Miller, Phillips-Twomey) regularization
Section 4.18.7.0 - Introductory Writeup
Section 4.18.7.0.1 - Basic Writeup - Wikipedia - Tikhonov Regularization
Section 4.18.7.0.2 - Detailed Writeup - Wieringen - Lecture notes on ridge regression
Section 4.18.7.1 - Tikhonov-regularized least squares
Section 4.18.7.2 - Generalized Tikhonov regularization
Section 4.18.7.3 - Lavrentyev regularization
Section 4.18.7.4 - Regularization in Hilbert space
Section 4.18.7.4.1 - Detailed Writeups
Section 4.18.7.4.1.1 - Part 1 - Rosasco - Reproducing Kernel Hilbert Spaces
Section 4.18.7.4.1.2 - Part 2 - Dinuzzo,Scholkopf - The representer theorem for Hilbert spaces: a necessary and sufficient condition
Section 4.18.7.5 - Relation to singular-value decomposition and Wiener filter
Section 4.18.7.6 - Relation to probabilistic formulation
Section 4.18.7.7 - Bayesian interpretation
Section 4.18.7.7.1 - Detailed Writeup - Frogner - Bayesian Interpretation of Regularization
Section 4.18.7.7.2 - Bayesian interpretation of Kernel Regularization
Section 4.18.8 - Early stopping
Section 4.18.8.0 - Introductory Writeups
Section 4.18.8.0.1 - Basic Writeup - Wikipedia - Early Stopping
Section 4.18.8.0.2 - Detailed Writeups
Section 4.18.8.0.2.1 - Part 1 - Prechelt - Early Stopping - But When?
Section 4.18.8.0.2.2 - Part 2 - Prechelt - Automatic Early Stopping Using Cross Validation: Quantifying the Criteria
Section 4.18.9 - Regularizers for sparsity
Section 4.18.9.0 - Basic Writeup - Wikipedia - Regularization: Regularizers for Sparsity
Section 4.18.9.1 - Proximal methods
Section 4.18.9.1.0 - Basic Writeup - Wikipedia - Proximal Gradient Method
Section 4.18.9.1.1 - Projected Landweber
Section 4.18.9.1.2 - Alternating projection
Section 4.18.9.1.3 - Alternating-direction method of multipliers
Section 4.18.9.1.4 - Fast Iterative Shrinkage Thresholding Algorithm (FISTA)
Section 4.18.9.2 - Group sparsity without overlaps
Section 4.18.9.3 - Group sparsity with overlaps
Section 4.18.9.4 - Structured Sparsity
Section 4.18.10 - Regularizers for semi-supervised learning
Section 4.18.10.0 - Introductory Writeups
Section 4.18.10.0.1 - Basic Writeup - Wikipedia - Regularization: Regularizers for Semi-Supervised Learning
Section 4.18.10.0.2 - Detailed Writeups
Section 4.18.10.0.2.1 - Part 1 - Chen,Wang - Regularized Boos for Semi-Supervised Learning
Section 4.18.10.0.2.2 - Part 2 - Sindhwani,Niyogi,Belki - A Co-Regularization Approach to Semi-Supervised Learning with Multiple Views
Section 4.18.10.0.2.3 - Part 3 - Li,Zemel - High Order Regularization for Semi-Supervised Learning of Structured Output Problems
Section 4.18.10.0.2.4 - Part 4 - Belkin,Matveeva,Niyogi - Regularization and Semi-supervised Learning on Large Graphs
Section 4.18.11 - Regularizers for multitask learning
Section 4.18.11.0 - Basic Writeup - Wikipedia - Regularization: Regularizers for Multi_Task Learning
Section 4.18.11.1 - Sparse regularizer on columns
Section 4.18.11.2 - Nuclear norm regularization
Section 4.18.11.3 - Mean-constrained regularization
Section 4.18.11.4 - Clustered mean-constrained regularization
Section 4.18.11.5 - Graph-based similarity
Section 4.18.11.5.0 - Introductory Writeups
Section 4.18.11.5.0.1 - Basic Writeup - Wikipedia - Regularization: Graph Based Similarity
Section 4.18.11.5.0.2 - Detailed Writeup - Li,Mark,Raskutti,Willet - Graph-based Regularization for Regression Problems with Highly-correlated Designs
Section 4.19 - Quantum Machine Learning - Derived From Quantum Computing and Machine Learning
Section 4.19.1 - Quantum Computing
Section 4.19.1.0 - Basic Writeups
Section 4.19.1.0.1 - Part 1 - Wikipedia - Quantum Computing
Section 4.19.1.0.2 - Part 2 - IBM - Quantum Computing Cloud Service
Section 4.19.1.1 - Principles of Operation
Section 4.19.1.2 - Quantum Operators
Section 4.19.1.2.0 Basic Writeup - Wikipedia - Quantum Operators
Section 4.19.1.2.1 - Wavefunction
Section 4.19.1.2.2 - Linear operators in wave mechanics
Section 4.19.1.2.3 - Commutation of operators on Ψ
Section 4.19.1.2.4 - Expectation values of operators on Ψ
Section 4.19.1.2.5 - Hermitian operators
Section 4.19.1.2.6 - Operators in matrix mechanics
Section 4.19.1.2.7 - Inverse of an operator
Section 4.19.1.2.8 - Table of QM operators
Section 4.19.1.2.9 - Examples of applying quantum operators
Section 4.19.2 - Quantum Algorithms
Section 4.19.2.0 - Basic Writeup - Wikipedia - Quantum Algorithm
Section 4.19.2.0.1 - Part 1
Section 4.19.2.0.2 - Part 2
Section 4.19.2.1 - Algorithms based on the quantum Fourier transform
Section 4.19.2.1.0 - Basic Writeup - Wikipedia - Quantum Fourier Transform
Section 4.19.2.1.1 - Deutsch–Jozsa algorithm
Section 4.19.2.1.2 - Simon's algorithm
Section 4.19.2.1.3 - Quantum phase estimation algorithm
Section 4.19.2.1.4 - Shor's algorithm
Section 4.19.2.1.5 - Abelian Hidden subgroup problem
Section 4.19.2.1.7 - Boson sampling problem
Section 4.19.2.1.8 - Estimating Gauss sums
Section 4.19.2.1.9 - Fourier fishing and Fourier checking
Section 4.19.2.2 - Algorithms based on amplitude amplification
Section 4.19.2.2.0 - Basic Writeup - Wikipedia - Amplitude Amplification
Section 4.19.2.2.1 - Grover's algorithm
Section 4.19.2.2.2 - Quantum counting
Section 4.19.2.3 - Algorithms based on quantum walks
Section 4.19.2.3.0 - Basic Writeup - Wikipedia - Quantum Walk
Section 4.19.2.3.1 - Element distinctness problem
Section 4.19.2.3.2 - Triangle-finding problem
Section 4.19.2.3.3 - Formula evaluation
Section 4.19.2.3.4 - Group commutativity
Section 4.19.2.4 - BQP-complete problems
Section 4.19.2.4.1 - Computing knot invariants
Section 4.19.2.4.2 - Quantum simulation
Section 4.19.2.4.3 - Solving Linear Systems of Equations
Section 4.19.2.4.4 - Linear Systems Algorithms
Section 4.19.2.4.5 - HHL Algorithm
Section 4.19.2.5 - Hybrid Quantum/Classical Algorithms
Section 4.19.2.5.1 - QAOA
Section 4.19.2.5.2 - Variational Quantum Eigensolver
Section 4.19.3 - Linear algebra simulation with quantum amplitudes
Section 4.19.4 - Quantum Machine Learning
Section 4.19.4.0 - Introductory Writeups
Section 4.19.4.0.1 - Basic Writeup - Wikipedia - Quantum Machine Learning Algorithms Based on Grover Search
Section 4.19.4.0.2 - Detailed Writeups
Section 4.19.4.0.2.1 - Part 1 - Biamonte,Wittek,Pancotti,Rebentrost,Wiebe,Llyod - Quantum Machine Learning
Section 4.19.4.0.2.2 - Part 2 - Textbook - Wittek - Quantum Machine Learning: What Quantum Computing Means to Data Mining
Section 4.19.4.1 - Quantum Learning Theory
Section 4.19.4.2 - Quantum Pattern Matching
Section 4.19.4.2.1 - Detailed Writeup - Mateus,Omar - Quantum Pattern Matching
Section 4.19.4.2.2 - Quantum Pattern Matching Fast On Average
Section 4.19.4.3 - Quantum Pattern Recognition
Section 4.19.4.3.0 - Introductory Writeups
Section 4.19.4.3.0.1 - Basic Writeups - Trungenberger - Quantum Pattern Recognition
Section 4.19.4.3.0.2 - Detailed Writeups
Section 4.19.4.3.0.2.1 - Part 1 - Trugenberger - Quantum Pattern Recognition
Section 4.19.4.3.0.2.2 - Part 2 - Sergioli,Santucci,Didaci,Miszczak,Giuntini - Pattern recognition on the quantum Bloch sphere
Section 4.19.4.4 - Quantum Optimization Algorithms
Section 4.19.4.5 - Quantum machine learning algorithms based on Grover search
Section 4.19.4.6 - Quantum-enhanced reinforcement learning
Section 4.19.4.6.0 - Introductory Writeups
Section 4.19.4.6.0.1 - Basic Writeup - Wikipedia - Quantum Reinforcement Learning
Section 4.19.4.6.0.2 - Detailed Writeup - Dunjko,Taylor,Briegel - Advances in Quantum Reinforcement Learning
Section 4.19.4.7 - Quantum annealing (Quantum Stochastic Optimization)
Section 4.19.4.7.0 - Introductory Writeups
Section 4.19.4.7.0.1 - Basic Writeup - Wikipedia - Quantum Annealing
Section 4.19.4.7.0.2 - Detailed Writeups
Section 4.19.4.7.0.2.1 - Part 1 - Falco,Tamascelli - An Introduction to Quantum Annealing
Section 4.19.4.7.0.2.2 - Part 2 - Ruiz - Quantum Annealing
Section 4.19.4.8 - Quantum sampling techniques
Section 4.19.4.8.0 - Introductory Writeups
Section 4.19.4.8.0.1 - Basic Writeup - Wikipedia - Quantum Sampling Techniques
Section 4.19.4.8.0.2 - Detailed Writeups
Section 4.19.4.8.0.2.1 - Part 1 - Lund,Bremner,Ralph - Quantum Sampling Problems, Boson Sampling and Quantum Supremacy
Section 4.19.4.8.0.2.2 - Part 2 - Maziero - Random Sampling of Quantum States: A suvey of Methods
Section 4.19.4.9 - Quantum neural networks
Section 4.19.4.9.0 - Introductory Writeups
Section 4.19.4.9.0.1 - Basic Writeup - Wikipedia - Quantum Neural Networks
Section 4.19.4.9.0.2 - Detailed Writeups
Section 4.19.4.9.0.2.1 - Part 1 - Farhii,Neven - Classification with Quantum Neural Networks on Near Term Processors
Section 4.19.4.9.0.2.2 - Part 2 - Ricks,Ventura - Training a Quantum Neural Network
Section 4.19.4.9.0.2.3 - Part 3 - Jia,Yi,Zhai,Wu,Guo,Guo - Quantum Neural Network States: A Brief Review of Methods and Applications
Section 4.19.4.9.0.2.4 - Part 4 - Chen - Quantum Neural Network and Soft Quantum Computing
Section 4.19.4.9.0.2.5 - Part 5 - Killoran,Bromley,Arrazola,Schuld,Quesda,Llyod - Continuous-variable Quantum Neural Networks
Section 4.19.4.10 - Hidden Quantum Markov Models
Section 4.19.4.10.0 - Introductory Writeups
Section 4.19.4.10.0.1 - Basic Writeup - Wikipedia - Hidden Quantum Markov Models
Section 4.19.4.10.0.2 - Detailed Writeups
Section 4.19.4.10.0.2.1 - Part 1 - Monras,Beige,Wiesner - Hidden Quantum Markov Models and Non-Adaptive Read-out of Many-Body States
Section 4.19.4.10.0.2.2 - Part 2 - Srinivasan,Gordon,Boots - Learning Hidden Quantum Markov Models
Section 4.19.4.10.0.2.3 - Part 3 - Clark,Huang,Barlow,Beige - Hidden Quantum Markov Models and Open Quantum Systems with Instantaneous Feedback
Section 4.19.4.11 - Fully quantum machine learning
Section 4.19.4.11.0 - Introductory Writeups
Section 4.19.4.11.0.1 - Basic Writeup - Wikipedia - Full Quantum Machine Learning
Section 4.19.4.11.0.2 - Detailed Writeup - Kopczyk - Quantum machine learning for data scientists
Section 4.19.4.12 - Classification with Quantum Machines
Section 4.19.4.12.1 - Detailed Writeups
Section 4.19.4.12.1.1 - Part 1 - Schuld,Sinayskiy,Petruccione - Quantum Computing for Pattern Classification
Section 4.19.4.12.1.2 - Part 2 - Farhi,Neven - Classification with Quantum Neural Networks
Section 4.19.4.12.1.3 - Part 3 - Schuld,Petruccione - Quantum Ensembles of Qunatum Classififers
Section 4.19.4.12.1.4 - Part 4 - Grant,Benedetti,Cao,Hallam,Lockhart,Stojevic,Green,Severini - Hierarchical Quantum Classififers
Section 4.19.4.13 - Quantum Recommendation Systems
Section 4.19.4.13.1 - Detailed Writeup - Kerenidis,Prakash - Quantum Recommendation Systems
Section 4.19.5 - Classical learning applied to quantum problems
Section 4.19.5.0 - Basic Writeup - Wikipedia - Classical Learning Applied to Quantum Problems
Section 4.19.5.1 - Noisy data
Section 4.19.5.2 - Calculated and noise-free data
Section 4.19.5.3 - Variational Circuits
Section 4.20 - Biases Impacting Machine Learning - Derived From Stastics, Computer Science and Behavioral Psychology
Section 4.20.1 - Categories of Bias
Section 4.20.1.1 - Cultural Bias
Section 4.20.1.2 - Statistical Bias
Section 4.20.1.3 - Cognitive Bias
Section 4.20.1.3.0 - Basic Writeups
Section 4.20.1.3.0.1 - Part 1 - Wikipedia - Cognitive Bias
Section 4.20.1.3.0.2 - Part 2 - Wikipedia - List of Cognitive Biases
Section 4.20.1.3.1 - Decision-making, belief, and behavioral biases
Section 4.20.1.3.2 - Social Biases
Section 4.20.1.3.3 - Memory errors and biases
Section 4.20.1.3.4 - Attribution Biases
Section 4.20.1.4 - Confirmation Bias
Section 4.20.2 - Types of Bias
Section 4.20.2.1 - Activity
Section 4.20.2.2 - Data
Section 4.20.2.3 - Sampling
Section 4.20.2.4 - Algorithmic or Automation
Section 4.20.2.5 - Presentation
Section 4.20.2.6 - Position
Section 4.20.2.7 - Social
Section 4.20.2.8 - Interaction
Section 4.20.2.9 - Latent
Section 4.20.2.10 - Self Selection
Section 4.20.2.11 - Second order
Section 4.21 - Accountable AI - Derived From Politics, Sociology and Governance
Section 4.21.0 - Basic Writeups
Section 4.21.0.1 - Part 1 - FATML - Principles for Accountable Algorithms
Section 4.21.0.2 - Part 2 - Stanford - AI and Life in 2030
Section 4.21.1 - Explainable AI
Section 4.21.1.0 - Introductory Writeups
Section 4.21.1.0.1 - Basic Writeups
Section 4.21.1.0.1.1 - Part 1 - Wikipedia - Explainable Artificial Intelligence
Section 4.21.1.0.1.2 - Part 2
Section 4.21.1.0.2 - Detailed Writeups
Section 4.21.1.0.2.1 - Part 1 - Samek,Wiegand,Muller - Explainable AI: Understanding, Visualizing and Interpretting Deep Learning Models
Section 4.21.1.0.2.2 - Part 2 - Darpa,Guning - Explainable AI (XAI)
Section 4.21.1.0.2.3 - Part 3 - Doran,Schulz,Besold - What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
Section 4.21.1.0.2.4 - Part 4 - Monaco - What is explainable AI and why is it important
Section 4.21.1.0.2.5 - Part 5 - Tjoa,Guan - A Survey of Explainable AI (XAI): toward Medical AI
Section 4.21.1.0.2.6 - Part 6 - Hall - Guidelines for Responsible and Human-Centered Use of Explainable Machine Learning
Section 4.21.1.0.2.7 - Part 7 - Abdul,Vermeulen,Wang,Lim,Kankanhalli - Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
Section 4.21.1.0.2.8 - Part 8 - Abdul,Wang,Lim,Yang - Designing Theory-Driven User-Centric Explainable AI
Section 4.21.1.0.3 - Code
Section 4.21.1.0.3.1 - Part 1 - Raphaël Meudec - TF-Explain
Section 4.21.1.0.3.2 - Part 2 - IBM - AI Explainability 360
Section 4.21.2 - Responsible AI
Section 4.21.2.0 - Introductory Writeups
Section 4.21.2.0.1 - Basic Writeup - PWC - Responsible AI
Section 4.21.2.0.2 - Detailed Writeups
Section 4.21.2.0.2.1 - Part 1 - Dignum - Responsible AI
Section 4.21.2.0.2.2 - Part 2 - D'Souza - Defining a Sandbox for Responsible AI
Section 4.21.3 - Accurate AI
Section 4.21.3.0 - Introductory Writeups
Section 4.21.3.0.1 - Basic Writeup - AI Impacts - Predictions of Human Level AI
Section 4.21.3.0.2 - Detailed Writeups
Section 4.21.3.0.2.1 - Part 1 - Armstrong,Sotala,Oh´Eigeartaigh - The errors, insights and lessons of famous AI predictions – and what they mean for the future
Section 4.21.3.0.2.2 - Part 2 - Venugopal,Gazettie,Gkuofas - Shadow Puppets: Cloud-level Accurate AI Inference at the Speed and Economy of Edge
Section 4.21.3.0.2.3 - Part 3 - Merler,Ratha,Feris,Smith - Diversity in Faces
Section 4.21.4 - Auditabile AI
Section 4.21.4.0 - Introductory Writeups
Section 4.21.4.0.1 - Basic Writeup - IIA - AI Auditing Framework: Global Perspectives and Insights
Section 4.21.4.0.2 - Detailed Writeup - Journal of Emerging technologies in Accounting - Research Ideas for AI in Auditing
Section 4.21.5 - Ethical, Transparent and Fair AI
Section 4.21.5.0 - Introductory Writeups
Section 4.21.5.0.1 - Basic Writeup - Eirini Malliaraki - Towards Ethical,Transparent and Fair AI/ML
Section 4.21.5.0.2 - Detailed Writeup
Section 4.21.5.0.2.1 - Part 1 - AI Fairness 360: An extensible toolkit for detecting, understanding and mitigating unwanted algorithmic bias
Section 4.21.5.0.2.2 - Part 2 - Vakkuri,Abrahamsson - The Key Concepts of Ethics of AI
Section 4.21.5.0.2.3 - Part 3 - Top 10 Principles for Ethical AI
Section 4.21.5.0.2.4 - Part 4 - Wang,Siau - Ethical and Moral Issues with AI: A Case study on Healthcare Robots
Section 4.21.5.0.2.5 - Part 5 - Hofheinz - The ethics of AI
Section 4.21.5.0.2.6 - Part 6 - Chander,Srinivansan,Chelian,Wang,Uchino - Working with Beliefs: AI Transparency in the Enterprise
Section 4.21.5.0.2.7 - Part 7 - Sethumadhavan,Levulis - Designing for Transparent AI
Section 4.21.5.0.2.8 - Part 8 - Goyal,Mohapatra,Parikh,Batra - Towards Transparent AI Systems: Interpretting Visual Question Answering Models
Section 4.21.5.0.2.9 - Part 9 - Geyik,Ambler,Kenthapadi - Fairness-aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
Section 4.21.5.0.2.10 - Part 10 - Sühr,Biega,Zehlike - Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform
Section 4.21.5.0.3 - Code
Section 4.21.5.0.3.1 - Part 1 - IBM - AI Fairness 360
Section 4.22 - Advanced Machine Learning - Derived From Behavioral Psych and Sociology
Section 4.22.1 - Transfer Learning
Section 4.22.1.0 - Introductory Writeups
Section 4.22.1.0.1 - Basic Writeups
Section 4.22.1.0.1.1 - Part 1 - Wikipedia - Transfer Learning
Section 4.22.1.0.1.2 - Part 2 - Ruder - Transfer Learning, Machine Learning's Next Frontier
Section 4.22.1.0.1.3 - Part 3 - Sarkar - A Comprehensive Hands-on Guide to Transfer Learning
Section 4.22.1.0.1.4 - Part 4 - Papers wtih Code - Transfer Learning
Section 4.22.1.0.2 - Detailed Writeups
Section 4.22.1.0.2.1 - Part 1 - Torrey,Shavlik - Transfer Learning
Section 4.22.1.0.2.2 - Part 2 - Weiss,Khoshgoftaar,Wang - A Survey of Transfer Learning
Section 4.22.1.0.2.3 - Part 3 - Tan,Sun,Kong,Zhang,Yang,Liu - A Survey on Deep Transfer Learning
Section 4.22.1.0.2.4 - Part 4 - Yosinki,Clne,Bengio,Lipson - How transferrable are features in deep neural networks
Section 4.22.2 - Multi-Task Learning
Section 4.22.2.0 - Introductory Writeups
Section 4.22.2.0.1 - Basic Writeups
Section 4.22.2.0.1.1 - Part 1 - Wikipedia - Multi-Task Learning
Section 4.22.2.0.1.2 - Part 2 - Ruder - Multi-Task Learning
Section 4.22.2.0.1.3 - Part 3 - Honchar - Multi-Task Learning: Teach your AI more to make it better
Section 4.22.2.0.1.4 - Part 4 - Ratner,Hancock,Ré - Emerging Topic in Multi-Task Learning Systems
Section 4.22.2.0.2 - Detailed Writeups
Section 4.22.2.0.2.1 - Part 1 - Zhang,Yang - A Survey on Multi-Task Learning
Section 4.22.2.0.2.2 - Part 2 - Caruana - A Survey on Multi-Task Learning
Section 4.22.2.0.2.3 - Part 3 - Meyerson,Miikkulainen - Beyond Shared Hierachies: Deep Multi-task Learning through Soft Layer Ordering
Section 4.22.2.0.2.4 - Part 4 - Ruder - An Overview of Multi-Task Learning in Deep Neural Networks
Section 4.22.2.0.2.5 - Part 5 - Sener,Koltun - Multi-Task Learning as Multi-Objective Optimization
Section 4.22.2.0.2.6 - Part 6 - Clark,Luon,Khandelwal,Manning,Le - BAM! Born-Again Multi-Task Networks for NLP
Section 4.22.2.0.2.7 - Part 7 - Fang,Ma,Zhang,Zhang,Bai - Dynamic Multi-Task Learning with CNN's
Section 4.22.3 - Meta Learning
Section 4.22.3.0 - Introductory Writeups
Section 4.22.3.0.1 - Basic Writeups
Section 4.22.3.0.1.1 - Part 1 - Wolf - From Zero to Research - An introduction to Meta Learning
Section 4.22.3.0.1.2 - Part 2 - Wikipedia - Meta Learning
Section 4.22.3.0.1.3 - Part 3 - Scholarpedia - Meta Learning
Section 4.22.3.0.1.4 - Part 4 - Joglekar - Understanding Few-Shot Intelligence as a Meta Learning problem
Section 4.22.3.0.2 - Detailed Writeups
Section 4.22.3.0.2.1 - Part 1 - Weng - Meta Learning
Section 4.22.3.0.2.2 - Part 2 - Finn,Rajeswaran,Kakade,Levine - Meta Learning
Section 4.22.3.0.2.3 - Part 3 - Finn,Levine - Probablistic Model-Agnostic Meta Learning
Section 4.22.3.0.2.4 - Part 4 - Vlialta,Carrier,Brazdil - Meta Learning: Concepts and Techniques
Section 4.22.4 - Continual Learning
Section 4.22.4.0 - Introductory Writeups
Section 4.22.4.0.1 - Basic Writeups
Section 4.22.4.0.1.1 - Part 1 - Lomonaco - Summary of the 2nd Continual Learning Workshop at NIPS 2018
Section 4.22.4.0.1.2 - Part 2 - Lomonaco - Articles on Continual AI
Section 4.22.4.0.2 - Detailed Writeups
Section 4.22.4.0.2.1 - Part 1 - Ven,Tolias - Three scenarios for continual learning
Section 4.22.4.0.2.2 - Part 2 - Parisi,Kemker,Part,Kanan,Wermter - Continual Lifelong Learning with Neural Networks: A Review
Section 4.22.4.0.2.3 - Part 3 - Ring - Toward a Formal Framework for Continual Learning
Section 4.22.4.0.2.4 - Part 4 - Chen,Liu - Lifelong Machine Learning: Continual Learning and Catastrophic Forgetting
Section 4.22.5 - Zero, One and Few Shot Learning
Section 4.22.5.0 - Introductory Writeups
Section 4.22.5.0.1 - Basic Writeups
Section 4.22.5.0.1.1 - One Shot
Section 4.22.5.0.1.1.1 - Part 1 - Wikipedia - One Shot Learning
Section 4.22.5.0.1.1.2 - Part 2 - Lamba - One Shot Learning with Siamese Networks using Keras
Section 4.22.5.0.1.1.3 - Part 3 - Bouma - One Shot Learning with Siamese Networks using Keras
Section 4.22.5.0.1.2 - Few Shot
Section 4.22.5.0.1.2.1 - Part 1 - Garbade - Understanding Few Shot learning in Machine Learning
Section 4.22.5.0.1.2.2 - Part 2 - Schwartz - Few-Shot Learning in CVPR 2019
Section 4.22.5.0.1.2.3 - Part 3 - Papers with Code - Few-Shot Learning
Section 4.22.5.0.2 - Detailed Writeups
Section 4.22.5.0.2.1 - Zero Shot
Section 4.22.5.0.2.1.1 - Part 1 - Xian,Lampert,Sciele,Akata - Zero Shot Learning - Comprehensive Evaluation of the good, the bad, and the ugly
Section 4.22.5.0.2.1.2 - Part 2 - Wang,Pang,Zhu,Li,Tian,Li - Visual Space Optimization for Zero Shot Learning
Section 4.22.5.0.2.1.3 - Part 3 - Huang,Wang,Yu,Wang - Generative Dual Adversarial Network for Generalized Zero Shot Learning
Section 4.22.5.0.2.1.4 - Part 4 - Wang,Zheng,Yu,Miao - A Survey of Zero Shot Learnings: Settings, Methods and Applications
Section 4.22.5.0.2.2 - One Shot
Section 4.22.5.0.2.2.1 - Part 1 - Fei-Fei,Fergus,Perona - One-Shot Learning of Object Categories
Section 4.22.5.0.2.2.2 - Part 2 - Koch,Zemel,Salakhutdinov - Siamese Neural Networks for One-shot Image Recognition
Section 4.22.5.0.2.2.3 - Part 3 - Vinyals,Blundell,Lillicrap,Kavukcuoglu,Weirstra - Matching Networks for One-shot Learning
Section 4.22.5.0.2.2.4 - Part 4 - Duan,Andrychowicz,Stadie,Ho,Schneider,Sutskever,Abbeel,Zaremba - One-shot Imitation Learning
Section 4.22.5.0.2.2.5 - Part 5 - Santoro,Bartunov,Botvinick,Wierstra,Lillicrap - One-shot Learning with Memory-Augmented Neural Network
Section 4.22.5.0.2.3 - Few Shot
Section 4.22.5.0.2.3.1 - Part 1 - Wang,Yao,Kwok,Ni - Generalizing from a few examples: A Survey of Few-Shot Learning
Section 4.22.5.0.2.3.2 - Part 2 - Karlinsky,Shtok,Harary,Schwart,Aides,Feris - RepMet: Representative-based metric learning for classification and few-shot object detection
Section 4.22.5.0.2.3.3 - Part 3 - Zhang,Lin,Liu,Yao,Shen - CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Section 4.22.5.0.2.3.4 - Metric Learning Methods
Section 4.22.5.0.2.3.4.1 - Part 1 - Li,Wang,Xu,Huo,Gao,Luo - Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Section 4.22.5.0.2.3.4.2 - Part 2 - Wertheimer,Hariharan - Few-Shot Learning with Localization in Realistic Settings
Section 4.22.5.0.2.3.4.3 - Part 3 - Wertheimer,Hariharan - Few-Shot Learning with Localization in Realistic Settings
Section 4.22.5.0.2.3.4.4 - Part 4 - Lifchitz,Avrithis,Picard,Bursuc - Dense Classification and Implanting for Few-Shot Learning
Section 4.22.5.0.2.3.4.5 - Part 5 - Kim,Oh,Lee,Pan,Kweon - Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
Section 4.22.5.0.2.3.5 - Meta Learning Methods
Section 4.22.5.0.2.3.5.1 - Part 1 - Lee,Maji,Ravichandran,Soatto - Meta-Learning with Differentiable Convex Optimization
Section 4.22.5.0.2.3.5.2 - Part 2 - Kim,Kim,Kim,Yoo - Edge-Labeling Graph Neural Network for Few-shot Learning
Section 4.22.5.0.2.3.5.3 - Part 3 - Jamal,Qi,Shah - Task Agnostic Meta-Learning for Few-Shot Learning
Section 4.22.5.0.2.3.5.4 - Part 4 - Sun,Liu,Chua,Schiele - Meta-Transfer Learning for Few-Shot Learning
Section 4.22.5.0.2.3.5.5 - Part 5 - Gidaris,Komodakis - Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning
Section 4.22.5.0.2.3.5.6 - Part 6 - Li,Eigen,Dodge,Zeiler,Wang - Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Section 4.22.5.0.2.3.6 - Data Augmentation Methods
Section 4.22.5.0.2.3.6.1 - Part 1 - Alfassy,Karlinksy,Aides,Shtok,Harary,Feris,Giryes,Bronstein - LaSO: Label-Set Operations networks for multi-label few-shot learning
Section 4.22.5.0.2.3.6.2 - Part 2 - Zhang,Zhang,Koniusz - Few-shot Learning via Saliency-guided Hallucination of Samples
Section 4.22.5.0.2.3.6.3 - Part 3 - Chu,Li,Chang,Wang - Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification
Section 4.22.5.0.2.3.6.4 - Part 4 - Chen,Fu,Wang,Ma,Liu,Hebert - Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification
Section 4.22.5.0.2.3.7 - Semantics-Based Methods
Section 4.22.5.0.2.3.7.1 - Part 1 - Schwartz,Karlinksy,Feris,Geryis,Bronstein - Baby steps towards few-shot learning with multiple semantics
Section 4.22.5.0.2.3.7.2 - Part 2 - Schönfeld,Ebrahimi,Sinha,Darrell,Akata - Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Section 4.22.5.0.2.3.7.3 - Part 3 - Wang,Wang,Darell,Gonzalez - TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning
Section 4.22.5.0.2.3.7.4 - Part 4 - Li,Luo,Lu,Xiang,Wang - Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy
Section 4.22.6 - Incremental Learning
Section 4.22.6.0 - Introductory Writeups
Section 4.22.6.0.1 - Basic Writeup - Wikipedia - Incremental Learning
Section 4.22.6.0.2 - Detailed Writeups
Section 4.22.6.0.2.1 - Part 1 - Castro,Jimenez,Guil,Schmid,Alahari - End-to_end Incremental Learning
Section 4.22.6.0.2.2 - Part 2 - Losing,Hammer,Wersing - Incremental Online Learning: A Review and Comparison of the State of the Art Algorithms
Section 4.22.6.0.2.3 - Part 3 - Bouchachia,Vanaret - Incremental Learning Based on Growing Gaussian Mixture Models
Section 4.22.6.0.2.4 - Part 4 - Liu,Chen - Incremental batch learning with support vector machines
Section 4.22.6.0.2.5 - Part 5 - Ruping - Incremental Learning with Support Vector Machiness
Section 4.22.6.0.2.6 - Part 6 - Muhlbaier,Topalis,Polikar - Learn++.MT: A New Approach to Incremental Learning
Section 4.22.6.0.2.7 - Part 7 - Ralaivola,Alche-Buc - Incremental support vector machine learning: A local approach
Section 4.22.6.0.2.8 - Part 8 - Gepperth,Hammer - Incremental learning algorithms and applications
Section 4.22.7 - AutoML
Section 4.22.7.0 - Introductory Writeups
Section 4.22.7.0.1 - Basic Writeups
Section 4.22.7.0.1.1 - Part 1 - AutoML.org - Automl
Section 4.22.7.0.1.2 - Part 2 - Wikipedia - Automated Machine Learning
Section 4.22.7.0.1.3 - Part 3 - MLJAR - Automated Machine Learning Software List
Section 4.22.7.0.1.4 - Part 4 - OpenML - Automated Machine Learning Benchmark
Section 4.22.7.0.2 - Detailed Writeups
Section 4.22.7.0.2.1 - Part 1 - Zoph,Vasudevan,Shlens,Le - Learning Transferable Architectures for Scalable Image Recognition
Section 4.22.7.0.2.2 - Part 2 - He,Zhao,Chu - AutoML: A Survey of the State-of-the-Art
Section 4.22.7.0.2.3 - Part 3 - Feurer,Klein,Eggensperger,Springenberg,Blum,Hutter - Efficient and Robust Automated Machine Learning
Section 4.22.7.0.2.4 - Part 4 - AutoML.org - Automated Machine Learning: Methods, Systems, Challenges
Section 4.22.7.0.2.5 - Part 5 - HiBayesian - Awesome AutoML Papers
Section 4.22.8 - Plasticity
Section 4.22.0 - Introductory Writeups
Section 4.22.0.1 - Basic Writeups
Section 4.22.0.1.1 - Wikipedia - Neuroplasticity
Section 4.22.0.2 - Detailed Writeups
Section 4.22.0.2.1 - Allam - Achieving Neuroplasticity in ANNs through Smart Cities
Section 4.22.0.2.2 - Perwej,Perwej - A Neuroplasticity (Brain Plasticity) approach in use in ANN
Section 4.22.0.2.3 - Miconi,Clune,Stanley - Differentiable plasticity: training plastic neural networks with backpropagation
