Machine Learning
2:40pm–3:55pm
COMPUTER SCIENCE BLDG CSC 451
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
Description
CC/GS: Partial Fulfillment of Science Requirement Basic statistical principles and algorithmic paradigms of supervised machine learning. Prerequisites: Multivariable calculus (e.g. MATH1201 or MATH1205 or APMA2000), linear algebra (e.g. COMS3251 or MATH2010 or MATH2015), probability (e.g. STAT1201 or STAT4001 or IEOR3658 or MATH2015), discrete math (COMS3203), and general mathematical maturity. Programming and algorithm analysis (e.g. COMS 3134). COMS 3770 is recommended for students who wish to refresh their math background.
Reviews
Course experience
n=49, 2003–2026
Aggregated across everyone who has taught this course.
Teaching quality
3.6 / 5
Grading fairness
4.5 / 5
Workload
4.0 / 5 · Heavy
Difficulty
3.5 / 5 · Hard
Overall sentiment
Mixed
In your week
Where it meets
Flat map — motion reduced