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 4 Overview
Classification
- Discussion 2 (solution)
- Homework 1 (zip)
- Lecture 9/13 (slides)
- Lecture 9/15 (slides)
- Lecture 9/15 (slides)
Week 5 Overview
Gradient Descent and Backpropagation
- Discussion 3 (solution)
- Homework 1 (zip)
- Homework 2 (zip)
- Lecture 9/22 (slides)
Week 6 Overview
Evaluating Models
- Discussion 4 (solution)
- Homework 2 (zip)
- Lecture 9/27 (slides)
- Lecture 9/29 (slides)
Week 7 Overview
Dimensionality Reduction
- Discussion 5 (solution)
- Homework 2 (zip)
- Homework 3
- Lecture 10/4 (slides)
- Lecture 10/6 (slides)
Week 8 Overview
Clustering
- Discussion 6 (solution)
- Homework 3
- Lecture 10/11 (slides)
- Lecture 10/13 (slides)
Week 9 Overview
Decision Trees
- Discussion 7 (solution)
- Homework 4
- Lecture 10/18 (slides)
- Lecture 10/20 (slides)
Week 10 Overview
Convolutional Neural Networks and Vision
- Discussion 8 (solution)
- Homework 4
- Lecture 10/25 (slides)
Week 11 Overview
Transformers and Natural Language Processing
Week 12 Overview
Graph Neural Networks and Computational Biology
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 (solution)
- Discussion 6: PCA (solution)
- Discussion 7: MLE/MAP Review (solution)
- Discussion 8: Decision Trees (solution)
- Discussion 9: Convolution, Transformers (solution)
- Discussion 10: PGM, MDP (solution)
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Homeworks
- Homework 0: Math review and self-assessment (zip)
- Homework 1: Gaussians, MLEs, Linear Regression (zip)
- Homework 2: Implementing a neural network in NumPy (zip)
- Homework 3: Decision theory, Bias-variance tradeoff
- Homework 4: Dimensionality reduction, K-nn, Trees
- Homework 5: MLPs, CNNs and Transformer on MNIST
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Lecture Slides and Videos
- 8/25: Welcome and Introduction
(slides) (slides) - 8/30: Maximum Likelihood Estimation
(slides) - 9/1: Multivariate Gaussians
(slides) - 9/6: Linear Regression I
(slides) - 9/8: Linear Regression II
(slides) - 9/13: Logistic Regression I
(slides) - 9/15: Neural Networks
(slides) - 9/15: Neural Networks I
(slides) - 9/22: Neural Networks II
(slides) - 9/27: Decision Theory
(slides) - 9/29: Bias/Variance, Overfit
(slides) - 10/4: Dimensionality Reduction, PCA
(slides) - 10/6: PCA, tSNE
(slides) - 10/11: Clustering
(slides) - 10/13: Nearest Neighbors
(slides) - 10/18: Decision Trees, Ensembles
(slides) - 10/20: CNN, ResNet
(slides) - 10/25: Attention, Transformers
(slides)
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