Summer 2025 Computer Science W4771 section 001

MACHINE LEARNING

Call Number 10755
Day & Time
Location		 TR 1:00pm-4:10pm
833 Seeley W. Mudd Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor Nakul Verma
Type LECTURE
Method of Instruction In-Person
Course Description

 

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.

Web Site Vergil
Subterm 07/07-08/15 (B)
Department Computer Science
Enrollment 26 students (120 max) as of 5:06PM Saturday, November 8, 2025
Subject Computer Science
Number W4771
Section 001
Division Interfaculty
Note https://www.cs.columbia.edu/~djhsu/coms4771-f25/#list-of-pre
Section key 20252COMS4771W001