CS229: Machine Learning

Winter 2025


Instructors


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.
Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of CS106A, CS106B, or CS106X, familiarity with probability theory to the equivalency of CS 109, MATH151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205. Please seet pset0 on ED.
Quick Links
All links will require a Stanford email to access. Course documents are only shared with Stanford University affiliates.
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
Head Course Assistant
Aman Patel
Computational Biology, General ML
Course Assistants
Shiny Weng
Statistical Learning, Trustworthiness, Computer Vision
Zipeng Fu
Robot Learning, Reinforcement Learning
Jacob Frausto
Computer Vision, Reinforcement Learning, LLM agents
Minsik Oh
Natural Language Processing, LLM Post-training, Representation Learning
Thomas Chen
ML theory
Amy Guan
Statistical Learning
Priya Khandelwal
Computer Vision, GNNs, ML Systems, LLMs
John So
Robot Learning, RL, CV
Arya B.
NLPs, GNNs, Systems, Robotics, CV