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Course Offerings

Course Evaluation Results

Course Details

Fall 2017-2018
* COS 324   Graded A-F, P/D/F, Audit

Introduction to Machine Learning

Elad Hazan
Yoram Singer

The course provides an introduction to machine learning. Topics covered: learning from examples and generalization. Empirical risk minimization and regularization. Introduction to convex analysis. Gradient-based learning. Implementation and analysis of learning algorithms for regression, binary classification, multiclass categorization, and ranking problems. Dimensionality reduction methods. Ensemble methods and boosting.

Sample reading list:
S. Shalev-Shwartz & S. Ben-David, Understanding Machine Learning: From Theory to Algorithms
M. Kearns & U. Vazirani, An Introduction to Computational Learning Theory
T. Michell, Machine Learning
K. Murphy, Machine Learning: A Probabilistic Perspective

Reading/Writing assignments:
Problem sets and programming exercises.

Requirements/Grading:
Mid Term Exam - 20%
Final Exam - 40%
Design Project - 20%
Problem set(s) - 20%

Prerequisites and Restrictions:
MAT 202 Linear Algebra COS 226: Algorithms and Data Structures Recommended: Multivariate Calculus, MAT 201 or a similar course Introductory course in Probability, ORF 309 or similar.

Website:  http://www.cs.princeton.edu/courses/archive/fall2017/cos324

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22909 L01 11:00 am - 12:20 pm T Th        Enrolled:113 Limit:130
22910 P01 7:30 pm - 8:20 pm Th        Enrolled:25 Limit:25 Closed
22992 P01A 7:30 pm - 8:20 pm Th        Enrolled:29 Limit:30
22911 P02 1:30 pm - 2:20 pm F        Enrolled:24 Limit:25
22984 P03 2:30 pm - 3:20 pm F        Enrolled:25 Limit:25 Closed
23015 P03A 2:30 pm - 3:20 pm F        Enrolled:10 Limit:25