CS229 - Machine Learning 코스 레퍼런스 (Stanford)

강의 링크
렉처 노트 링크

강의 소개

2018년 가을 학기에 진행된 CS229 머신러닝 렉처이다.

커리큘럼

• Supervised Learning setup, linear regression
• Linear algebra
• Weighted least squares, Logistic regression, Newton’s method, Perceptron, Exponential Family, Generalized Linear Models
• Probability
• Gaussian Discriminant Analysis, Naive Bayes
• Laplace Smoothing. Support Vector Machines
• Python
• Support Vector Machines. Kernels.
• Bias-Variance tradeoff. Regularization and model/feature selection.
• Learning theory
• Tree ensembles.
• Neural Networks:basics
• Neutral Networks:Training
• Evaluation metrics
• Practical advice for ML projects
• K-means. Mixture of Gaussians. Expectation Maximization.
• Factor analysis.
• Principal Component Analysis. Independent Component Analysis.
• MDPs. Bellman Equations
• Value iteration and Policy iteration. LQR. LQG.
• Q-learning. Value function approximation.
• Policy Serach. REINFORCE. POMDPs.