Personal notes for course CS229 Machine Learning @ Stanford 2020 Spring
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Course Website: CS229: Machine Learning
| Date | Description | Written Notes | Public Class Notes |
|---|---|---|---|
| 4/8 | Supervised Learning Setup Linear Regression Normal Equation |
Supervised Learning, Discriminative Algorithms [pdf] | |
| 4/13 | Weighted Least Squares Exponential Family Netwon's Method |
Linear Algebra Review and Reference [pdf] | |
| 4/15 | Perceptron Logistic Regression Generalized Linear Models |
||
| 4/20 | Generative Learning Algorithms Naive Bayes |
Generative Algorithms [pdf] | |
| 4/22 | Laplace Smoothing Support Vector Machines |
Support Vector Machines [pdf] | |
| 4/27 | Kernel Methods & SVM | ||
| 4/29 | Neural Networks | Deep Learning [pdf] | |
| 5/4 | Backpropagation | ||
| 5/6 | Bias & Variance Regularization Feature & Model selection |
Regularization and Model Selection [pdf] | |
| 5/11 | K-Means Mixture of Gaussians |
Unsupervised Learning, k-means clustering [pdf] Mixture of Gaussians [pdf] |
|
| 5/13 | EM Algorithm Factor Analysis |
The EM Algorithm [pdf] | |
| 5/18 | Factor Analysis PCA |
Lagrange Multipliers Review [pdf] Factor Analysis [pdf] |
|
| 5/20 | PCA ICA |
Principal Components Analysis [pdf] Independent Component Analysis [pdf] |
|
| 5/27 | Weak Supervision | Weak Supervision [pdf (slides)] | |
| 6/1 6/3 |
Markov Decision Process Value Iteration and Policy Iteration Q-Learning Value function approximation |
- | Reinforcement Learning and Control [pdf] |
| 6/8 | Policy search Reinforce POMDPs |
- | Policy Gradient (REINFORCE) [pdf] |