[ToDo] CS 229: Machine Learning

Introduction and Basic Concepts

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

Naïve Bayes

Laplace Smoothing

Support Vector Machines

Python and Numpy

Support Vector Machines

Kernels

Neural Networks

Bias - Variance

Regularization

Feature/Model Selection

Deep Learning

K-Means

GMM (non EM)

Expectation Maximization

Factor Analysis

Principal and Independent Component Analysis

Weak Supervision

Markov Decision Process

Value Iteration and Policy Iteration

Q-Learning

Value Function Approximation

Reinforcement Learning

Policy Search

Reinforce

POMDPs

Fairness

Adversarial

Online Learning and the Perceptron Algorithm

Binary Classification with +/-1 Labels

The Representer Theorem

Hoeffding’s Inequality

Decision Trees

Boosting Algorithms and Weak Learning

On Critiques of ML