CS229 - Machine Learning 코스 레퍼런스 (Stanford)

Posted on Edited on In , Views:

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.