All lecture notes in one PDF: All lecture notes

Front matter: Title page, table of contents, notation

	Topic	Contents	Links
1.	Introduction to Machine Learning	Overview of topics	Machine Learning (Wikipedia)
2.	Linear Regression	1D regression Multidimensional regression	Linear Regresion (Wikipedia)
3.	Nonlinear Regression	Radial Basis Functions Neural Networks K-nearest neighbors	RBFs (Wikipedia) ANNs (Wikipedia) KNN (Wikipedia)
4.	Quadratics
5.	Basic Probability Theory
6.	Probability Density Functions (PDFs)	PDFs Mean and covariance Uniform distribution Gaussian distribution	PDFs (Wikipedia)
7.	Estimation	Bayes Rule MAP Maximum Likelihood
8.	Classification	Class conditionals Logistic Regression Neural nets for classification Naïve Bayes	Logistic Regression (Wikipedia) Naïve Bayes (Wikipedia)
9.	Gradient Descent	Gradient descent Line Search	Gradient descent (Wikipedia) Line Search (Wikipedia) Optimization (Wikipedia)
10.	Cross Validation	Hold-out Validation N-Fold Cross Validation	Cross-validation (Wikipedia)
11.	Bayesian Methods	Bayesian Regression Relationship to Maximum Likelihood Regression Hyperparameters Model Selection	Bayesian model selection demos (Tom Minka)
12.	Monte Carlo Methods	Sampling Gaussians Importance Sampling Metropolis Hastings	MCMC (Wikipedia) MCMC applet
13.	Principal Components Analysis	PCA Whitening PPCA	PCA (Wikipedia)
14.	Lagrange Multipliers	Equality constraints Bounds constraints	Lagrange Multipliers (Wikipedia)
15.	Clustering	K-means Mixtures of Gaussians Expectation-Maximization	K-means (Wikipedia)
16.	Hidden Markov Models	Markov chains Viterbi Forward-Backward Baum-Welch (EM)	HMMs (Wikipedia)
17.	Support Vector Machines	Maximum margin Loss functions Kernels	SVMs (Wikipedia)
18.	AdaBoost	Boosting Exponential loss Early stopping	Tutorial slides on AdaBoost