Course Overview
This 8-week course provides an introduction to Artificial Intelligence (AI) and Machine Learning (ML) at a high-school level, combining foundational intuition and hands-on critical thinking for students interested in machine learning applications. Through engaging visual learning tools and interactive demonstrations, students will explore key topics including data collection, supervised and unsupervised learning, and real-world applications of deep learning.
📅 Course Information
- Time: Fridays (Mar 13 – May 8, 2026)
- Location:290 Hearst Mining Building, UC Berkeley
- Class Time: 4:30 – 6:00 PM
- Prerequisites: Pre-Calculus, Geometry (Calculus AB helpful but not required)
✅ Passing Requirements
- Attend all lectures
- Complete homework assignments (graded on completion)
- Contribute meaningfully to group project
💻 Technology
- Computer for homework and projects
- Pen and paper for assignments
- Canvas/Google Classroom for submissions
Learning Goals
- Understand the Foundations of AI and ML - Define AI, ML, and Deep Learning, and explore their real-world applications
- Gain Intro to Core Mathematical Concepts - Understand mathematical principles and optimization techniques in ML
- Foster Collaboration and Teamwork - Work in teams to design and present AI/ML projects
- Bring Intuition into Mathematics - Foster creativity to solve problems in your communities (most important goal!)
Group Project
In the final weeks, you'll work on a collaborative group project. Choose from:
- Teach a ML Topic: Present on a machine learning topic not covered in class
- Study ChatGPT: Explore and analyze ChatGPT or similar AI systems
- Student-Proposed Project: Propose your own project (requires instructor approval)
Weekly Schedule
All lectures are in-person. Attendance at all sessions is required for certificate completion.
| Week | Topic | Materials | Homework |
|---|---|---|---|
| 1 Mar 13 |
Introduction – Goals, Data & Decision Making Lecture: Intro to ML, course goals Discussion: Math Review, "What can we do with lines?" |
Pre-course survey | |
| 2 Mar 20 |
Classification I – Binary & Linear Classifiers Lecture: Binary classification, linear classifiers Discussion: Decision Trees & Multiclass Classification |
Build your own Decision Tree(s) | |
| 3 Mar 27 |
Classification II – Neural Networks & ReLU Lecture: Neural Networks, ReLU activation Discussion: Model building, feature selection |
No Homework! | |
| 4 Apr 3 |
Linear Regression – Setup & Limitations Lecture: Linear regression setup Discussion: Overfitting and Loss Functions |
Linear Regression Concept Check | |
| 5 Apr 10 |
Clustering – Unsupervised Learning Lecture: Clustering basics Discussion: Project Specs review |
Clustering Labubu's | |
| 6 Apr 24 |
Guest Lecture Generative models & LLMs |
Work on project | |
| 7 May 1 |
ML in Context – Degrees, Ethics & Safety Lecture: ML degrees and careers Discussion: Ethics, alignment, deepfakes |
Continue project (Jailbreaking GPT) | |
| 8 May 8 |
Project Presentations / LLMs Lecture: Project presentations Discussion: How to make use of ChatGPT? |
Lecture
Discussion
|
Done! 🎉 |
Course Topics Covered
- Converting Data to Features
- Feature Selection
- Linear Classifiers
- Decision Trees
- Neural Networks
- Activation Functions
- Linear Regression
- Loss Functions
- Overfitting
- Clustering
- Ethics and AI Safety
- Understanding Probabilities*
- Support Vector Machines*
- Logistic Regression*
- Large Language Models*
* Topics marked with asterisk may be adjusted based on student feedback
Course Resources
📚 Online Platforms
- Google Classroom - Main submission platform
- Zoom Links - Virtual office hours and discussions
🔧 Tools We'll Use
- Desmos - Interactive graphing for regression analysis
- Pen & Paper - Most assignments are hands-on!
- Your creativity - The most important tool
📖 Additional Learning Materials
🎥 Lecture Recordings
- All Recordings Playlist
- Recordings posted within 24 hours of each lecture
Course Staff
Meet the team behind Foundations of Machine Learning!
Lecture & Discussion Instructors
Kenny Wongchamcharoen
Lecture/Discussion Instructor
B.S. in Industrial Engineering & Operations Research (IEOR), Data Science & Math @ Berkeley
Email: pattaraphon.kenny@berkeley.edu
Office Hours: TBA
Andrew Chan
Lecture/Discussion Instructor
B.S. in Analytics, M.S. in Industrial Engineering & Operations Research (IEOR) @ Berkeley
Email: andrewjchan@berkeley.edu
Office Hours: TBA
Project Instructor
Samantha Lee
Project Instructor
B.S. in Analytics, M.S. in Industrial Engineering & Operations Research (IEOR) @ Berkeley
Email: salee2@berkeley.edu
Office Hours: TBA
Faculty Advisor
Phillip Kerger
Faculty Advisor
Assistant Teaching Professor @ Berkeley IEOR
Email: kerger@berkeley.edu
Office Hours: TBA
⏰ Office Hours
Office hours will be posted on Google Classroom. Zoom links will be provided there.
Join your instructors and peers to discuss course material, ask questions, or just chat! Even if you don't have questions, feel free to join and listen in. Office hours are a great way to get to know your instructors and classmates.