CS229 main notes.pdf

CS229: Machine Learning

https://github.com/hws2002/cs229-Machine_Learning-2018

Lecture1

Lecture2 - Linear Regression and Gradient Descent

Lecture3 - Locally Weighted & Logistic Regression + Newton’s Method

Lecture4 - Perceptron & Generalized Linear Model ⭐️

Lecture5 - Generative Learning Algorithms(GDA, Naive Bayes) ⭐️⭐️

Summary1

Lecture6 - Laplace smoothing & Support Vector Machines ⭐️⭐️

Lecture7 - Kernels

lecture8 - Data Splits, Models & Cross-Validation

Lecture9 - Approx/Estimation Error & ERM

Lecture10 - Decision Trees and Ensemble Methods

Lecture 15 - EM algorithm & Factor Analysis

Supplementary & Discussion Section

Learning theory

Binary Classification with +/-1 labels.

The Representer theorem (Mercer)