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.
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
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"
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"
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