UC Berkeley CS 189/289A: Intro to Machine Learning (Fall 2025)

This is Gary Agasa's learning resources and homework solutions of CS189 2025Fall Class.

Course materials and student resources for CS 189/289A. Course website: eecs189.org/fa25

Repository Structure

• lec: lecture demo notebooks
• hw: homeworks
• disc: discussion worksheets and notebooks

Setup

• Install Python dependencies:

pip install -r requirements.txt

Progress Tracking

Week 1

• Lecture 1: Introduction + ML Problem Framing

Week 2

• Lecture 2: Data Tools
• Discussion 1: Math Review & Data Processing
• Lecture 3: ML Mechanics, Terminology, and Techniques
• ==Homework 1: Intro to ML Released (Notebook, materials)==
• ==Homework 1: Written Math Refresher==

Week 3

• Lecture 4: K-Means and Probability
• Discussion 2: Machine Learning Design
• Lecture 5: Density Estimation and Gaussian Mixture Models

Week 4

• Lecture 6: GMM Recap and Linear Regression (1)
• Discussion 3: Probability, K-Means & MLE
• Lecture 7: Linear Regression (2)
• ==Homework 1 - Part 1 Due==
• ==Homework 1 - Written Due==

Week 5

• Lecture 8: Linear Regression (3)
• Discussion 4: GMM & Linear Regression
• Lecture 9: Bias-Variance Trade-off
• ==Homework 2: Chatbot Arena Released (Notebook, materials)==
• ==Homework 1 - Part 2 Due==

Week 6

• Lecture 10: Logistic Regression (1)
• Discussion 5: Regularization and Bias–Variance
• Lecture 11: Logistic Regression (2)
• ==Homework 2 - Part 1 Due==

Week 7

• Lecture 12: Optimization and Gradient Descent
• Discussion 6: Logistic Regression
• Lecture 13: Adam and Stochastic Gradient Descent

Week 8

• Lecture 14: Neural Networks and PyTorch
• Discussion 7: Gradient Descent
• Lecture 15: Pytorch Continued and Backpropagation
• ==Homework 3: Backprop and Neural Nets Released==
• ==Homework 2 - Part 2 Due==

Week 9

• Midterm Exam (7:00 - 9:00pm)
• No Discussion
• Lecture 16: Neural Network Design and Regularization

Week 10

• Lecture 17: NN + Architectures: CNN
• Discussion 8: Neural Network
• Lecture 18: Architectures: CNN

Week 11 (Current Week)

• Lecture 19: CNN and Autoencoder
• Discussion 9: NN Reg & CNNs
• Lecture 20: Transformers
• ==Homework 4: CNNs and Transformers Released==
• ==Homework 3 Due==

Week 12

• Veterans Day – No Lecture
• Discussion 10
• Lecture 21: LLM
• ==Homework 4: CNNs and Transformers==

Week 13

• Lecture 22: LLM
• Discussion 11
• Lecture 23: Guest
• ==Homework 4 - Part 1 Due==

Week 14

• Lecture 24: Self-Supervised Learning
• Discussion 12
• Thanksgiving – No Lecture
• ==Homework 5: Pre-training + Instruction-tuning Released==
• ==Homework 4 - Part 2 Due==

Week 15

• Lecture 25: Diffusion
• Discussion 13
• Lecture 26: Closing
• ==Homework 5: Pre-training + Instruction-tuning==

Week 16 – RRR Week

• ==Homework 5 Due==

Week 17 – Finals Week

• Final Exam (8:00-11:00 AM)