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  • Stanford CS229 Machine Learning by Andrew Ng

    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

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  • 原文地址:https://www.cnblogs.com/yaoyaohust/p/11094499.html
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