CS 189/289A: Intro to Machine Learning
UC Berkeley, Fall 2026
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- 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 1 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 (Current Week)
- Mon Aug 24
- No Discussion No Discussion
- Tue Aug 25
- No Lecture No Lecture
- Wed Aug 26
- Thu Aug 27
- Lecture 1 Introduction + ML Problem Framing
- Lecture Participation 1 Slido
- Fri Aug 28
Week 2
- Mon Aug 31
- Discussion ML Problem Framing
- Tue Sep 1
- Lecture 2 Data Tools + K-Means and KNN
- Wed Sep 2
- Thu Sep 3
- Lecture 3 Math Refresher + Probability + Linear Algebra
- Fri Sep 4
- Homework 1
Week 3
- Mon Sep 7
- Labor Day - Holiday
- Tue Sep 8
- Lecture 4 Density Estimation
- Wed Sep 9
- Discussion Math Refresher (Linear Algebra, Probability); K-Means and KNN
- Thu Sep 10
- Lecture 5 Gaussian Mixture Models
- Fri Sep 11
Week 4
- Mon Sep 14
- Discussion Density Estimation and GMMs
- Tue Sep 15
- Lecture 6 Linear Regression
- Wed Sep 16
- Thu Sep 17
- Lecture 7 Bias-Variance Trade-off + Regularization
- Fri Sep 18
- Homework 1 Part 1 due
Week 5
- Mon Sep 21
- Discussion Linear Regression + MLE Perspective
- Tue Sep 22
- Lecture 8 Logistic Regression (1)
- Wed Sep 23
- Thu Sep 24
- Lecture 9 Logistic Regression (2)
- Fri Sep 25
- Homework 2
- Homework 1 Part 2 due
Week 6
- Mon Sep 28
- Discussion Logistic Regression + Regularization + Bias/Variance
- Tue Sep 29
- Lecture 10 Gradient Descent (1)
- Wed Sep 30
- Thu Oct 1
- Lecture 11 Gradient Descent (2)
- Fri Oct 2
Week 7
- Mon Oct 5
- Discussion Gradient Descent
- Tue Oct 6
- Lecture 12 Neural Networks (1): Non-linearity, Architecture, Activation Functions, Output Layers, and Loss
- Wed Oct 7
- Thu Oct 8
- Lecture 13 Neural Networks (2): Backpropagation
- Fri Oct 9
- Homework 3
- Homework 2 due
Week 8
- Mon Oct 12
- Discussion Neural Networks
- Tue Oct 13
- Lecture 14 Neural Networks (3): Batch Normalization, Initialization, and Regularization
- Wed Oct 14
- Thu Oct 15
- Lecture 15 Neural Networks (4)
- Fri Oct 16
- Midterm Review Midterm Review
Week 9
- Mon Oct 19
- No Discussion No Discussion
- Tue Oct 20
- No Lecture No Lecture
- Midterm Exam Midterm (5:00–6:30 PM)
- Wed Oct 21
- Thu Oct 22
- Lecture 16 Neural Networks (5) + Architectures: CNN
- Fri Oct 23
- Homework 3 Part 1 due
Week 10
- Mon Oct 26
- Discussion CNNs
- Tue Oct 27
- Lecture 17 Architectures: CNN
- Wed Oct 28
- Thu Oct 29
- Lecture 18 Transformers
- Fri Oct 30
- Homework 4
- Homework 3 Part 2 due
Week 11
Fri Nov 6 :
Week 12
- Mon Nov 9
- Discussion LLMs
- Tue Nov 10
- Lecture 21 Attention Methods
- Wed Nov 11
- Veterans Day - Holiday
- Thu Nov 12
- Lecture 22 MDP, RL
- Fri Nov 13
- Homework 4 Part 1 due
Week 13
- Mon Nov 16
- Discussion LLMs / Attention and MDP/RL
- Tue Nov 17
- Lecture 23 RL
- Wed Nov 18
- Thu Nov 19
- Lecture 24 Guest Lecture: TBD
- Fri Nov 20
- Homework 5
- Homework 4 Part 2 due
Week 14
- Mon Nov 23
- No Discussion No Discussion
- Tue Nov 24
- Lecture 25 Post-training: Fine-tuning, LoRA, PEFT, and Distillation
- Wed Nov 25
- Thanksgiving - Holiday
- Thu Nov 26
- No Lecture No Lecture
Fri Nov 27 :
Week 15
Week 16
- Mon Dec 7
- Tue Dec 8
- Final Review Final Review
- Wed Dec 9
- Thu Dec 10
- Fri Dec 11
- Homework 5 Part 2 due
Week 17 - Finals Week
- Mon Dec 14
- Tue Dec 15
- Wed Dec 16
- Thu Dec 17
- Final Exam Final (11:30 AM - 2:30 PM, cumulative)
- Fri Dec 18