# CS229-Machine-Learning

**Repository Path**: bilibili-wyj/CS229-Machine-Learning

## Basic Information

- **Project Name**: CS229-Machine-Learning
- **Description**: Stanford CS229 course material by Andrew Ng, with problem set, Matlab code and scanned notes written by me
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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