zhangzongchen/cs229-machine-learning

Stanford CS229 course material by Andrew Ng, with problem set, Matlab code and scanned notes written by me

CS229-Machine-Learning

CS229 Machine Learning Online Course by Andrew Ng

Course material: CS229-Machine-Learning / MachineLearning / materials / aimlcs229 /

Problem set Matlab codes: CS229-Machine-Learning / MachineLearning / materials / aimlcs229 / Problem Sets / is written by me, except some prewritten codes by course providers.

Scanned notes about video course: CS229-Machine-Learning / MachineLearning / materials / aimlcs229 / YaoYaoNotes / is my notes about this video course.

Course material 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