Machine Learning, or, An Unofficial Guide to Georgia Institute of Technology's CS7641: Machine Learning

Contents
I Supervised Learning
Techniques
Classification
Decision Trees
Getting Answers
Asking Questions: The ID3 Algorithm
Considerations
Ensemble Learning
Bagging
Boosting
Support Vector Machines
There are lines and there are lines…
Support Vectors
Extending SVMs: The Kernel Trick
Summary
Regression
Linear Regression
Neural Networks
Perceptron
Sigmoids
Structure
Biases
Instance-Based Learning
Nearest Neighbors
Computational Learning Theory
Learning to Learn: Interactions
Space Complexity
Version Spaces
Error
PAC Learning
Epsilon Exhaustion
Infinite Hypothesis Spaces
Intuition
Vapnik-Chervonenkis Dimension
Information Theory
Entropy: Information Certainty
Joint Entropy: Mutual Information
Kullback-Leibler Divergence
Bayesian Learning
Bayesian Learning
Finding the Best Hypothesis
Finding the Best Label
Bayesian Inference
Bayesian Networks
Making Inferences
Naïve Bayes
II Unsupervised Learning
Randomized Optimization
Hill Climbing
Simulated Annealing
Genetic Algorithms
High-Level Algorithm
Cross-Over
Challenges
MIMIC
High-Level Algorithm
Estimating Distributions
Practical Considerations
Clustering
Single Linkage Clustering
Considerations
k-Means Clustering
Convergence
Considerations
Soft Clustering
Expectation Maximization
Considerations
Analyzing Clustering Algorithms
Properties
How Many Clusters?
Features
Feature Selection
Filtering
Wrapping
Describing Features
Feature Transformation
Motivation
Principal Component Analysis
Independent Component Analysis
Alternatives
III Reinforcement Learning
Markov Decision Processes
Bellman Equation
Finding Policies
Q-Learning
Game Theory
Games
Relaxation: Non-Determinism
Relaxation: Hidden Information
Prisoner's Dilemma
Nash Equilibrium
Summary
Uncertainty
Tit-for-Tat
Folk Theorem
Pavlov's Strategy
Coming Full Circle
Example: Grid World
Generalization
Solving Stochastic Games
Index of Terms