Machine Learning
Intro
A Quick Note on Notation and Terminology
Chapter 1: Classification and ML Basics
Training and Testing
Comparing Classification Models
The MNIST dataset
Validation
Supervised vs. Unsupervised Learning
Summary
Chapter 2: Linear Classifiers
Behind the Decision Boundary
Linear Separability of Data
The Centroid Method
The Perceptron Algorithm
Perceptron Algorithm, Part 2
The Perceptron Algorithm, Part 3
Maximum Margin Classfier
Chapter 3: Support Vector Machines
Chapter 4: Optimization Problems
Chapter 5: Decision Theory
Chapter 6: Gaussian Discriminant Analysis
Chapter 7: Eigenvectors and Quadratic Forms
Chapter 8: Anisotropic Gaussians
Chapter 9: Regression
Chapter 10: Statistical Justifications for Regression
Chapter 11: Regularization
Chapter 12: Decision Trees
Lecture 15: Decision Trees Cont.
Chapter 13: Kernels
Chapter 14: Neural Networks
Neural Network Variations
Chapter 15: Improving Neural Network Training
Chapter 16: Unsupervised Learning and PCA
Chapter 17: Singular Value Decomposition and Clustering
Chapter 18: Spectral Graph Clustering
Chapter 19: Multiple Eigenvectors
Chapter 20: AdaBoost
Chapter 21: Nearest Neighbors
Chapter 22: Optomizing k-NN
Machine Learning Glossary
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Index
