Slides 1, Introduction

Slides 2, Notes 2, The Bayes classifier

Slides 3, Notes 3, A first look at the theory of learning

Slides 4, k-nearest neighbor classification

Slides 5, plugin methods: LDA and logistic regression
   Notes on the convergence of gradient descent and Newton’s method

Slides 6, linear classifiers: the perceptron and maximum margin

Slides 7, Notes 7, kernels

Slides 8, overview of constrained optimization

Slides 9, Notes 9, support vector machines

Slides 10, Notes 10, A second look at the theory of learning

Slides 11, Notes 11, regression and regularization

Slides 12, bias vs. variance

Slides 13, validation and model selection

Slides 14, neural nets

Slides 15, feature selection

Slides 16, clustering, K-means, GMMs, EM algorithm

Slides 17, Notes 17, principal components analysis (PCA)

Slides 18, nonlinear dimensionality reduction (MDS, ISOMAP, LLE)

Notes 19, spectral clustering

Notes 20, structured matrix factorization