Lecture 1Machine Learning Course Introduction

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Lecture 1: Machine Learning Course Introduction

Machine Learning Course Introduction

Lecture 2: Linear Regression and Gradient Descent

Lecture 3: Locally Weighted and Logistic Regression

Lecture 4: Perceptron and Generalized Linear Model

Lecture 5: GDA and Naive Bayes

Lecture 6: Support Vector Machines

Lecture 7: Kernels

Lecture 8: Data Splits, Models and Cross-Validation

Discussion Section: Learning Theory

Lecture 9: Decision Trees and Ensemble Methods

Lecture 10: Introduction to Neural Networks

Lecture 11: Backprop and Improving Neural Networks

Lecture 12: Debugging ML Models and Error Analysis

Lecture 13: Expectation-Maximization Algorithms

Lecture 14: EM Algorithm and Factor Analysis

Lecture 15: PCA and ICA

Lecture 16: Independent Component Analysis and RL

Lecture 17: MDPs and Value/Policy Iteration

Lecture 18: Continuous State MDP and Model Simulation

Lecture 19: Reward Model and Linear Dynamical System

Lecture 20: RL Debugging and Diagnostics