Introduction to Machine Learning

University of California, Berkeley, Fall 2023

Welcome to CS 189/289A! This class covers theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; deep learning models including CNNs, Transformers, graph neural networks for vision and language tasks; and Markovian models for reinforcement learning and robotics.

Here are the Gradescope/Ed codes (you should self-enroll in these). We won’t post any materials on bCourses.

Also, the Professors will post slides prior to lecture at this Google Drive folder (for faster access). The material here is redundant with the website, but it may take up to a day or two for the website to get updated with the slides after lecture. The “Post-Lecture” subfolder contains updates to slides that the Professors may make right after lecture.

Note: The topics for future lectures, discussions, and HWs are tentative and may be moved around, changed, or removed.

Overview

Week 1

8/24 Lecture 1 Introduction and Logistics Slides / Recording

Week 2

8/28 Homework 1 Math Review (Due 9/8 11:59pm) PDF
8/29 Lecture 2 Maximum Likelihood Estimation Slides / Recording
8/30 Discussion 0 Math Pre-Requisites Review PDF / Solutions
8/31 Lecture 3 Multivariate Gaussians Slides / Recording

Week 3

9/5 Lecture 4 Linear Regression 1 Slides / Recording
9/6 Discussion 1 MLE & Gaussians PDF / Solutions
9/7 Lecture 5 Linear Regression 2 Slides / Recording
9/9 Homework 2 Linear/Logistic Regression & Classification (Due 9/22 11:59pm) PDF

Week 4

9/12 Lecture 6 Classification - Generative & Discriminative Slides / Recording
9/13 Discussion 2 Linear Regression PDF / Solutions
9/14 Lecture 7 Logistic Regression & Neural Networks Slides / Recording

Week 5

9/19 Lecture 8 Backpropagation and Gradient Descent 1 Slides / Recording
9/20 Discussion 3 Classification & Logistic Regression PDF / Solutions / Walkthrough
9/21 Lecture 9 Backpropagation and Gradient Descent 2 Handout / Recording
9/23 Homework 3 Neural Networks (Due 10/6 11:59pm) PDF / Files

Week 6

9/26 Lecture 10 Neural Networks - CNNs, Batch Norm, & ResNets Slides / Recording
9/27 Discussion 4 Neural Networks and Training PDF / Solutions / Walkthrough
9/28 Lecture 11 Neural Networks - Attention & Transformers Slides / Recording

Week 7

10/3 Lecture 12 Dimensionality Reduction & PCA Slides / Recording
10/4 Discussion 5 Convolution and Attention PDF / Solutions / Walkthrough
10/5 Lecture 13 t-SNE Slides / Recording
10/7 Homework 4 Dimensionality Reduction & Decision Theory (Due 10/20 11:59pm) PDF / Files

Week 8

10/10 Lecture 14 Clustering Slides / Recording
10/11 Discussion 6 Dimensionality Reduction Techniques PDF / Solutions
10/12 Lecture 15 Multiway Classification, Decision Theory, & Model Evaluation Slides / Recording
10/13 Midterm (7-9pm, Pimentel 1) Blank / Solutions

Week 9

10/17 Lecture 16 Nearest Neighbors & Metric Learning Slides / Recording
10/18 Discussion 7 Clustering and Decision Theory PDF / Solutions / Walkthrough
10/19 Lecture 17 Decision Trees & Ensembling Slides / Recording
10/21 Homework 5 Bias/Variance, Nearest Neighbors, Decision Trees (Due 11/3 11:59pm) PDF / Files

Week 10

10/24 Lecture 18 Bias-Variance Tradeoff & Over/Under-Fitting Slides / Recording
10/25 Discussion 8 Bias/Variance and Nearest Neighbors PDF / Solutions / Walkthrough
10/26 Lecture 19 Hidden Markov Models & Graphical Models 1 Slides / Handout / Recording

Week 11

10/31 Lecture 20 Hidden Markov Models & Graphical Models 2 Slides / Handout / Recording
11/1 Discussion 9 Decision Trees and HMMs Intro PDF / Solutions / Walkthrough
11/2 Lecture 21 Markov Decision Processes Slides / Recording
11/4 Homework 6 Markovian Models & Reinforcement Learning (Due 11/17 11:59pm) PDF / Files

Week 12

11/7 Lecture 22 Reinforcement Learning Recording
11/8 Discussion 10 HMMs Advanced and MDPs PDF / Solutions
11/9 Lecture 23 Robotics and Machine Learning Recording

Week 13

11/14 Lecture 24 Graph Neural Networks & Rotational Equivariance 1 Slides / Recording
11/15 Discussion 11 MDPs & Reinforcement Learning PDF / Solutions / Walkthrough
11/16 Lecture 25 Graph Neural Networks & Rotational Equivariance 2 Slides / Recording
11/18 Homework 7 Graph Neural Networks & Applications of Deep Learning (Due 12/1 11:59pm) PDF / Files

Week 14

11/21 Lecture 26 Language and Vision Applications Recording
11/23 No Lecture (Thanksgiving)  

Week 15

11/28 Lecture 27 Special Topics - Causality Slides / Recording
11/29 Discussion 12 Graph Neural Networks PDF / Solutions / Walkthrough
11/30 Lecture 28 Special Topics - Computational Biology Slides

Week 16 (RRR Week)

No lectures or discussions this week

Week 17 (Finals Week)

12/12 Final Exam (8-11am, Location TBD) Blank / Solutions