CS229 Machine Learning Stanford Course by Andrew Ng

Course material, problem set Matlab code written by me, my notes about video course：

https://github.com/Yao-Yao/CS229-Machine-Learning

Contents：

• supervised learning

Lecture 1

application field, pre-requisite knowledge

supervised learning, learning theory, unsupervised learning, reinforcement learning

Lecture 2

linear regression, batch gradient decent, stochastic gradient descent(SGD), normal equations

Lecture 3

locally weighted regression(Loess), probabilistic interpretation, logistic regression, perceptron

Lecture 4

Newton's method, exponential family(Bernoulli, Gaussian), generalized linear model(GLM), softmax regression

Lecture 5

discriminative vs  generative, Gaussian discriminent analysis, naive bayes, Laplace smoothing

Lecture 6

multinomial event model, nonlinear classifier, neural network, support vector machines(SVM), functional margin/geometric margin

Lecture 7

optimal margin classifier, convex optimization, Lagrangian multipliers, primal/dual optimization, KKT complementary condition, kernels

Lecture 8

Mercer theorem, L1-norm soft margin SVM, convergence criteria, coordinate ascent, SMO algorithm

• learning theory

Lecture 9

underfit/overfit, bias/variance, training error/generalization error, Hoeffding inequality, central limit theorem(CLT), uniform convergence, sample complexity bound/error bound

Lecture 10

VC dimension, model selection, cross validation, structured risk minimization(SRM), feature selection, forward search/backward search/filter method

Lecture 11

Frequentist/Bayesian, online learning, SGD, perceptron algorithm, "advice for applying machine learning"

• unsupervised learning

Lecture 12

k-means algorithm, density estimation, expectation-maximization(EM) algorithm, Jensen's inequality

Lecture 13

co-ordinate ascent, mixture of Gaussian(MoG), mixture of naive Bayes, factor analysis

Lecture 14

principal component analysis(PCA), compression, eigen-face

Lecture 15

latent sematic indexing(LSI), SVD, independent component analysis(ICA), "cocktail party"

• reinforcement learning

Lecture 16

Markov decision process(MDP), Bellman's equations, value iteration, policy iteration

Lecture 17

continous state MDPs, inverted pendulum, discretize/curse of dimensionality, model/simulator of MDP, fitted value iteration

Lecture 18

state-action rewards, finite horizon MDPs, linear quadratic regulation(LQR), discrete time Riccati equations, helicopter project

Lecture 19

"advice for applying machine learning"-debug RL algorithm, differential dynamic programming(DDP), Kalman filter, linear quadratic Gaussian(LQG), LQG=KF+LQR

Lecture 20

partially observed MDPs(POMDP), policy search, reinforce algorithm, Pegasus policy search, conclusion