CS229: Machine Learning

Instructors

Moses Charikar
Carlos Guestrin
Andrew Ng

Course Description   This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.


Course Information

Time and Location
Instructor Lectures: Mon, Wed 1:30 PM - 2:50 PM (PT) at Gates B1 Auditorium
CA Lectures: Please check the Syllabus page or the course's Canvas calendar for the latest information.
Quick Links
Contact and Communication
Ed is the primary method of communication for this class. Please do NOT reach out to the instructors (or course staff) directly, otherwise your questions may get lost. Due to a large number of inquiries, we encourage you to first read the Course Logistics and FAQ document for commonly asked questions, and then create a post on Ed to contact the course staff.
This quarter we will be using Ed as the course forum.
  • All official announcements and communication will happen over Ed.
  • Any questions regarding course content and course organization should be posted on Ed. You are strongly encouraged to answer other students' questions when you know the answer.
  • If there are private matters specific to you (e.g. special accommodations, requesting alternative arrangements etc.), please create a private post on Ed.
  • For longer discussions with TAs, please attend office hours.
  • TA office hours can be found on Canvas. For the course calendar, see also Canvas and the Syllabus and Course Materials page.
  • Before the beginning of the course, please contact the head TA for logistical questions (ideally after consulting the FAQ link).

Course Staff

To help with project advice, each member of course staff's ML expertise is also listed below.

Course Manager
John Cho
Head Course Assistant
Zhoujie Ding
NLP, LLMs
Course Assistants
Neil Band
LLMs, uncertainty quantification
Ed Chen
multi-objective optimization, preference learning, healthcare
Ryan A. Chi
NLP, LLM reasoning, dialogue
Ryan Li
NLP, Large Language Models, Agentic AI
Chris Fifty
multimodal / meta / multi-task / transfer learning
Charlie Marx
uncertainty quantification, probabilistic modeling, time series, diffusion
Roshni Sahoo
causal inference
Boxin Zhang
physical sciences, statistical learning
Paris Zhang
CV, VLMs, visual generative models
Kamyar Salahi
NLP, VLMs, Generative Models, LLMs
Sergio Charles
Geometric Deep Learning, GNNs, Generative Models
Roger Dai
Embodied AI, Robot Learning, CV
Medhanie Irgau
Transfer Learning, Generative Models, Uncertainty Quantification
Tejas Narayanan
CV, RL