CS 189 (CDSS) at UC Berkeley
Introduction to Machine Learning
Lectures: Monday, Wednesday, & Friday 1-2 pm in Evans 10 (Berkeley Academic Guide page)
Week 1 Overview
Welcome and Introduction
- Homework 0
- Lecture 1/19 (slides) (video)
- Lecture 1/21 (slides) (video)
Week 2 Overview
Maximum Likelihood Estimation
- Discussion 0 (solution)
- Homework 0
- Lecture 1/24 (slides) (video)
- Lecture 1/26 (slides) (video)
- Lecture 1/28 (slides) (video)
Week 3 Overview
Linear Regression and MAP Estimation
- Discussion 1 (solution)
- Homework 1
- Lecture 1/31 (slides) (video)
- Lecture 2/2 (slides) (video)
- Lecture 2/4 (slides) (video)
Week 4 Overview
Logistic Regression and Optimization
- Discussion 2 (solution)
- Homework 1
- Lecture 2/7 (slides) (video)
- Lecture 2/9 (slides) (video)
- Lecture 2/11 (slides) (video)
Week 5 Overview
Support Vector Machines
- Discussion 3 (solution)
- Homework 2 (zip)
- Lecture 2/14 (slides) (video)
- Lecture 2/16 (slides) (video)
- Lecture 2/18 (slides) (video)
Week 6 Overview
Kernels
- Discussion 4 (solution)
- Homework 2 (zip)
- Lecture 2/23 (slides) (video)
- Lecture 2/25 (slides) (video)
Week 7 Overview
Analytical Techniques in ML
- Discussion 5 (solution)
- Homework 3 (zip)
- Lecture 2/28 (slides) (video)
- Lecture 3/2 (slides) (video)
- Lecture 3/4 (slides) (video)
Week 9 Overview
Nearest Neighbors and Decision Trees
- Discussion 7 (solution)
- Homework 3 (zip)
- Lecture 3/14 (slides) (video)
- Lecture 3/16 (slides) (video)
- Lecture 3/18 (slides) (video)
Week 10 Overview
Dimensionality Reduction and Clustering
- Discussion 8 (solution)
- Homework 4 (zip)
- Lecture 3/28 (slides) (video)
- Lecture 3/30 (slides) (video)
- Lecture 4/1 (slides) (video)
Week 11 Overview
Neural Networks
- Discussion 9 (solution)
- Homework 4 (zip)
- Homework 5 (link)
- Lecture 4/4 (slides) (video)
- Lecture 4/6 (slides) (video)
- Lecture 4/8 (slides) (video)
Week 12 Overview
Convolutional Neural Networks
- Discussion 10 (solution)
- Homework 5 (link)
- Lecture 4/11 (slides) (video)
- Lecture 4/13 (slides) (video)
- Lecture 4/15 (slides) (video)
Week 13 Overview
Transformers
- Discussion 11 (solution)
- Homework 6
- Lecture 4/18 (slides) (video)
- Lecture 4/20 (slides) (video)
- Lecture 4/22 (slides) (video)
Week 14 Overview
Special Topics
- Discussion 12 (solution)
- Homework 6
- Lecture 4/25 (slides) (video)
- Lecture 4/27 (slides) (video)
- Lecture 4/29 (slides) (video)
Discussions
- Discussion 0: Math review and self-assessment (solution)
- Discussion 1: Maximum Likelihood Estimation (solution)
- Discussion 2: MAP Estimation and LASSO Regression (solution)
- Discussion 3: Logistic Regression and Gaussian Discriminative Analysis (solution)
- Discussion 4: Support Vector Machines (solution)
- Discussion 5: Kernels (solution)
- Discussion 6: Midterm Review (solution)
- Discussion 7: Risk Minimization and Bias Variance (solution)
- Discussion 8: Decision Trees and K-Nearest Neighbors (solution)
- Discussion 9: Principal Component Analysis (solution)
- Discussion 10: Neural Networks (solution)
- Discussion 11: Convolutional Neural Networks (solution)
- Discussion 12: Attention and Transformers (solution)
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Homeworks
- Homework 0: Math review and self-assessment
- Homework 1: Multivariate Gaussians and Regression
- Homework 2: Logistic Regression, GDA, and SVMs (zip)
- Homework 3: Kernels (zip)
- Homework 4: Decision Trees and K-Nearest Neighbors (zip)
- Homework 5: PCA and Neural Networks (link)
- Homework 6: CNNs and Transformers
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Lecture Slides and Videos
- 1/19: Welcome and Introduction
(slides) (video) - 1/21: Math Review
(slides) (video) - 1/24: MLE 1
(slides) (video) - 1/26: MLE 2
(slides) (video) - 1/28: Linear Regression 1
(slides) (video) - 1/31: Multivariate Gaussians
(slides) (video) - 2/2: MAP Estimation
(slides) (video) - 2/4: Linear Regression 2
(slides) (video) - 2/7: Classification for Gaussians
(slides) (video) - 2/9: Logistic Regression
(slides) (video) - 2/11: Optimization
(slides) (video) - 2/14: SVMs 1
(slides) (video) - 2/16: SVMs 2
(slides) (video) - 2/18: SVMs 3
(slides) (video) - 2/23: Kernels 1
(slides) (video) - 2/25: Kernels 2
(slides) (video) - 2/28: Validation Sets
(slides) (video) - 3/2: Bias-Variance and Decision Theory
(slides) (video) - 3/4: Precision, Recall, and ROC Curves
(slides) (video) - 3/14: Nearest Neighbors
(slides) (video) - 3/16: Decision Trees 1
(slides) (video) - 3/18: Decision Trees 2
(slides) (video) - 3/28: PCA 1
(slides) (video) - 3/30: PCA 2
(slides) (video) - 4/1: Clustering
(slides) (video) - 4/4: Neural Networks 1
(slides) (video) - 4/6: Neural Networks 2
(slides) (video) - 4/8: Neural Networks 3
(slides) (video) - 4/11: Convolutional Neural Networks 1
(slides) (video) - 4/13: Convolutional Neural Networks 2
(slides) (video) - 4/15: OPTIONAL: Unsupervised Learning Methods
(slides) (video) - 4/18: Transformers 1
(slides) (video) - 4/20: Transformers 2
(slides) (video) - 4/22: Transformers 3
(slides) (video) - 4/25: Guest Speaker: Recommender Systems
(slides) (video) - 4/27: Guest Speaker: Fairness
(slides) (video) - 4/29: OPTIONAL: Finale
(slides) (video)
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