CS229: Machine Learning

Winter 2026


Instructor

Emily Fox
Sanmi Koyejo

Course Description   This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative learning, parametric/non-parametric learning, neural networks); unsupervised learning (clustering, dimensionality reduction); 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 10:30 AM - 11:50 AM NVIDIA 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 or CS106B, familiarity with probability theory to the equivalency of CS 109 or MATH151, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205L. Please see 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.
  • For 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).
AIWG Statement
This course is participating in the proctoring pilot overseen by the Academic Integrity Working Group (AIWG). The purpose of this pilot is to determine the efficacy of proctoring and develop effective practices for proctoring in-person exams at Stanford. To find more details on the pilot or the working group, please visit the AIWG’s webpage.
OAE Deadlines Statement
IMPORTANT OAE DEADLINES: If you plan to use your OAE-approved exam accommodations for a specific assessment, students must provide their letter and inform the instructor by:
  • 10 calendar days prior to a midterm or quiz date.
You only need to submit your letter once per quarter. For urgent OAE-related accommodation needs that arise after the deadline, please consult your OAE adviser. If you are not yet registered with OAE, contact the office directly at [email protected]

Course Staff

Course Manager
John Cho
Head Course Assistant
Andrew Shi
Course Assistants
Hercy
Hercy Shen
Michael
Michael Li
Natalie
Natalie Dullerud
Salman
Salman Abdullah
Shreyas
Shreyas Agarwal
Suchir
Suchir Agarwal
Tae
Tae Hoon Yang
Yash
Yash Kankariya
Course Advisor
Swati Dube Batra