CS 189/289A: Intro to Machine Learning
UC Berkeley, Fall 2025
Ed Gradescope Lectures Playlist Additional Accommodations Office Hours Queue Content Repository Class Drive
- Our course staff email is cs189-instructors@berkeley.edu. This email is monitored by the instructors, the head TAs, and a few lead TAs.
Welcome to Week 4 of CS 189/289A!
Lectures will be broadcast at this link.
Please note that the size of this course is not expanding, and we cannot predict whether you will get off the waitlist.
Schedule
Week 1
- Wed Aug 27
- First Day of Classes
- Thu Aug 28
- Lecture 1 Introduction + ML Problem Framing
- Lecture Participation 1 Slido
- Fri Aug 29
Week 2
- Mon Sep 1
- Tue Sep 2
- Lecture 2 Data Tools
- Lecture Participation 2 Slido
- Wed Sep 3
- Discussion 1 Introduction to ML
- Thu Sep 4
- Lecture 3 ML Mechanics, Terminology, and Techniques
- Lecture Participation 3 Slido
- Fri Sep 5
- Homework 1 Intro to ML (notebook, materials)
- Homework 1 Written Math Refresher
Week 3
- Mon Sep 8
- Tue Sep 9
- Lecture 4 K-Means and Probability
- Lecture Participation 4 Slido
- Wed Sep 10
- Discussion 2 Working with Data & ML Techniques
- Thu Sep 11
- Lecture 5 Density Estimation and Gaussian Mixture Models
- Lecture Participation 5 Slido
Week 4 (Current Week)
- Mon Sep 15
- Tue Sep 16
- Lecture 6 GMM Recap and Linear Regression (1)
- Lecture Participation 6 Slido
- Wed Sep 17
- Discussion 3 Probability & KMeans & Linear Regression
- Thu Sep 18
- Lecture 7 Linear Regression (2)
- Fri Sep 19
- Homework 1 - Part 1 and Homework 1 Written Due
Week 5
- Mon Sep 22
- Tue Sep 23
- Lecture 8 Linear Regression (3)
- Wed Sep 24
- Discussion 4 Linear Regression & MLE
- Thu Sep 25
- Lecture 9 Logistic Regression (1)
- Fri Sep 26
- Homework 2 Chatbot Arena
- Homework 1 - Part 2 due
Week 6
- Mon Sep 29
- Tue Sep 30
- Lecture 10 Logistic Regression (1)
- Wed Oct 1
- Discussion 5 Logistic Regression & MLE
- Thu Oct 2
- Lecture 11 Gradient Descent (1)
- Fri Oct 3
- Homework 2 Part 1 due
Week 7
- Mon Oct 6
- Tue Oct 7
- Lecture 12 Gradient Descent (2)
- Wed Oct 8
- Discussion 6 Gradient Descent
- Thu Oct 9
- Lecture 13 Neural Networks (1): Build Non-linearity, Architecture, Activation Functions
- Fri Oct 10
Week 8
- Mon Oct 13
- Tue Oct 14
- Lecture 14 Neural Networks (2): PyTorch NN and Backpropagation
- Wed Oct 15
- Discussion 7 Neural Networks Basics
- Thu Oct 16
- Lecture 15 Neural Networks (3): Batch Normalization, Initialization, and Regularization
- Fri Oct 17
- Homework 3 Backprop and Neural Nets
- Homework 2 Part 2 due
Week 9
- Mon Oct 20
- Tue Oct 21
- Midterm Exam Midterm (7:00 - 9:00pm)
- Wed Oct 22
- Discussion 8 Neural Networks & Backpropagation
- Thu Oct 23
- Lecture 16 Architectures: CNN
- Fri Oct 24
- Homework 3 - Part 1 due
Week 10
- Mon Oct 27
- Tue Oct 28
- Lecture 17 Architectures: RNN
- Wed Oct 29
- Discussion 9 CNN
- Thu Oct 30
- Lecture 18 Transformers
- Fri Oct 31
- Halloween
Week 11
- Mon Nov 3
- Tue Nov 4
- Lecture 19 Generative models - NLP (LLM)-1
- Wed Nov 5
- Discussion 10 RNN Architecture and Transformer
- Thu Nov 6
- Lecture 20 Generative models - NLP (LLM)-2
- Fri Nov 7
- Homework 4 CNNs and Transformers
- Homework 3 Part 2 due
Week 12
- Mon Nov 10
- Tue Nov 11
- Veterans Day No Lecture
- Wed Nov 12
- Discussion 11 Transformers & LLMs
- Thu Nov 13
- Lecture 21 Guest Lecture - Prof. Efros
- Fri Nov 14
- Homework 4 - Part 1
Week 13
- Mon Nov 17
- Tue Nov 18
- Lecture 22 Dimensionality Reduction (PCA)
- Wed Nov 19
- Discussion 12 Generative Models & NLP
- Thu Nov 20
- Lecture 23 Auto-encoder + GAN basic (1)
- Fri Nov 21
Week 14
- Mon Nov 24
- Tue Nov 25
- Lecture 24 Auto-encoder + GAN basic (2)
- Wed Nov 26
- Discussion 13 PCA & Auto-encoders
- Thu Nov 27
- Thanksgiving No Lecture
- Fri Nov 28
- Homework 5 Pre-training + instruction-tuning
- Homework 4 - Part 2 due
Week 15
- Mon Dec 1
- Tue Dec 2
- Lecture 25 Guest Lecture
- Wed Dec 3
- Discussion 14 GANs & Course Review
- Thu Dec 4
- Lecture 26 Closing
- Fri Dec 5
Week 16 - RRR Week
- Mon Dec 8
- Finals Week
- Tue Dec 9
- Study Day
- Wed Dec 10
- Study Day
- Thu Dec 11
- Study Day
- Fri Dec 12
- Homework 5 DUE
Week 17 - Finals Week
- Tue Dec 16
- Final Exam Final (8:00-11:00 AM)