About & Angel Club
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"Machine Learning (CS 229) Stanford"
Description: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Professors: Michael Williams
Machine Learning Syllabus
Lec 1 Introduction
Lec 2 Linear regression, gradient descent, normal equations
Lec 3 Linear reg, Locally weighted reg, probabilistic interp, logisitic regres, Digression perception, Newton's method
Lec 4 Newton's method, exponential families, and generalized linear models
Lec 5 generative learning algorithms and Gaussian discriminative analysis
Lec 6 naive Bayes, neural networks, and support vector machine
Lec 7 optimal margin classifiers, KKT conditions, and SUM duals
Lec 8 support vector machines, including soft margin optimization and kernels
Lec 9 learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities
Lec 10 learning theory by discussing VC dimension and model selection
Lec 11 Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms
Lec 12 context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.
Lec 13 expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.
Lec 14 factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).
Lec 15 principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.
Lec 16 reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.
Lec 17 reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations.
Lec 18 state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs.
Lec 19 debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning.
Lec 20 POMDPs, policy search, and Pegasus in the context of reinforcement learning