CS 189/289A at UC Berkeley
Introduction to Machine Learning
Lectures: Tuesday, Thursday 2-3:30 pm in Li Ka Shing 245 (Berkeley Academic Guide page)
Week 2 Overview
Maximum Likelihood Estimation
- Discussion 0 (solution)
- Homework 0 (zip)
- Lecture 8/30 (slides)
- Lecture 9/1 (slides)
Week 3 Overview
Linear Regression
- Discussion 1 (solution)
- Homework 0 (zip)
- Homework 1 (zip)
- Lecture 9/6 (slides)
- Lecture 9/8 (slides)
Week 5 Overview
Gradient Descent and Backpropagation
Week 9 Overview
Decision Trees
Week 10 Overview
Convolutional Neural Networks and Vision
Week 11 Overview
Transformers and Natural Language Processing
Week 12 Overview
Graph Neural Networks and Computational Biology
Week 13 Overview
Hidden Markov Models and Graphical Models
Week 14 Overview
Reinforcement Learning
Week 15 Overview
Robotics and Causality
Discussions
- Discussion 0: Math review and self-assessment (solution)
- Discussion 1: Maximum Likelihood Estimation, Regularization, LASSO (solution)
- Discussion 2: MLE vs. MAP, Regularization (solution)
- Discussion 3: Gradient Descent, Neural Networks (solution)
- Discussion 4: Backpropagation (solution)
- Discussion 5: Risk Minimization, Bias-Variance Tradeoff
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