CS 189/289A at UC Berkeley
Introduction to Machine Learning
Lectures: Tues & Thurs 2-3:30 pm online (Berkeley Academic Guide page)
Week 2 Overview
Maximum Likelihood and Multivariate Gaussians
- Discussion 0 (solution)
- Homework 0
- Lecture 8/31 (slides) (video)
- Lecture 9/2 (slides) (video)
Week 3 Overview
Linear Regression and Classification
- Discussion 1 (solution)
- Homework 1 (zip)
- Lecture 9/7 (slides) (video)
- Lecture 9/9 (slides) (slides) (video)
Week 4 Overview
Logistic Regression, Neural Networks, Backpropagation, and SGD
- Discussion 2 (solution)
- Homework 1 (zip)
- Lecture 9/14 (slides) (slides) (video)
- Lecture 9/16 (slides) (video)
Week 5 Overview
Multiway Classification, Decision Theory, Bias-Variance, Over- and Under-Fitting
- Discussion 3 (solution)
- Homework 2 (zip)
- Lecture 9/21 (slides) (slides) (video)
- Lecture 9/23 (slides) (video)
Week 6 Overview
ROC Curves, Precision/Recall, Dimensionality Reduction, and PCA
- Discussion 4 (solution)
- Homework 2 (zip)
- Lecture 9/28 (slides) (video)
- Lecture 9/30 (slides) (video)
Week 7 Overview
t-SNE and Clustering
- Discussion 5 (solution)
- Homework 3
- Lecture 10/5 (slides) (video)
- Lecture 10/7 (slides) (video)
Week 9 Overview
Support Vector Machines, Nearest Neighbor, and Metric Learning
- Discussion 7 (solution)
- Homework 4 (zip)
- Lecture 10/19 (slides) (video)
- Lecture 10/21 (slides) (video)
Week 10 Overview
Vision and Convolutional Neural Networks
- Discussion 8 (solution)
- Homework 4 (zip)
- Lecture 10/26 (slides) (video)
- Lecture 10/28 (slides) (video)
Week 11 Overview
Decision Trees, Random Forests, and Transformers
- Discussion 9 (solution)
- Homework 5
- Lecture 11/2 (slides) (video)
- Lecture 11/4 (slides) (video)
Week 13 Overview
Applications in Biology and Hidden Markov Models
- Discussion 11 (solution)
- Homework 6
- Lecture 11/16 (slides) (video)
- Lecture 11/18 (slides) (video)
Week 15 Overview
Causality and Reinforcement Learning
- Final (Tues 12/14 8-11am) (logistics)
- Discussion 12 (solution)
- Homework 7
- Lecture 11/30 (slides) (video)
- Lecture 12/2 (slides) (video)
Discussions
- Discussion 0: Math review and self-assessment (solution)
- Discussion 1: MLE, linear regression, and MAP (solution)
- Discussion 2: MAP and regularized regression (solution)
- Discussion 3: Logistic regression and backpropagation (solution)
- Discussion 4: Decision theory and bias/variance (solution)
- Discussion 5: Principal components analysis (solution)
- Discussion 6: Midterm review (solution)
- Discussion 7: Clustering, kernel methods, PCA, and SVMs (solution)
- Discussion 8: More SVMs (solution)
- Discussion 9: Nearest neighbor and convolutions (solution)
- Discussion 10: Decision trees (solution)
- Discussion 11: PyTorch (solution)
- Discussion 12: Probabilistic graphical models (solution)
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Homeworks
- Homework 0: Math review and self-assessment
- Homework 1: MVGs, linear and logistic regression, and cross-validation (zip)
- Homework 2: Neural networks (zip)
- Homework 3: More classification and regression, and bias-variance
- Homework 4: Kernels, kernel ridge regression, and PCA (zip)
- Homework 5: SVMs, nearest neighbor, and decision trees
- Homework 6: Convolutional neural networks and transformers
- Homework 7: Potpourri
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Lecture Slides and Videos
- 8/26: Welcome and Introduction
(slides) (slides) (video) - 8/31: Maximum Likelihood Estimation
(slides) (video) - 9/2: Multivariate Gaussians
(slides) (video) - 9/7: Linear Regression
(slides) (video) - 9/9: Linear Regression continued, Classification
(slides) (slides) (video) - 9/14: Logistic Regression, Neural Networks
(slides) (slides) (video) - 9/16: Neural Networks, Backpropagation and SGD
(slides) (video) - 9/21: Multiway Classification, Decision Theory
(slides) (slides) (video) - 9/23: Bias-variance, Over- and under-fitting
(slides) (video) - 9/28: ROC Curves, Precision/Recall
(slides) (video) - 9/30: Dimensionality Reduction, PCA
(slides) (video) - 10/5: t-SNE
(slides) (video) - 10/7: Clustering
(slides) (video) - 10/12: Kernel Methods
(slides) (video) - 10/14: SVMs part 1
(slides) (video) - 10/19: SVMs part 2
(slides) (video) - 10/21: Nearest Neighbor and Metric Learning
(slides) (video) - 10/26: Biological Vision
(slides) (video) - 10/28: Convolutional Neural Networks
(slides) (video) - 11/2: Decision Trees and Random Forests
(slides) (video) - 11/4: Transformers part 1
(slides) (video) - 11/9: Transformers part 2
(slides) (video) - 11/16: ML + Biology
(slides) (video) - 11/18: Hidden Markov Models
(slides) (video) - 11/23: Probabilistic Graphical Models
(slides) (video) - 11/30: Causality
(slides) (video) - 12/2: Reinforcement Learning
(slides) (video)
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