Skip over navigation

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.

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.


Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22909 L01 11:00:00 am - 12:20:00 pm T Th   Computer Science Building   104   Enrolled:119 Limit:154
22910 P01 07:30:00 pm - 08:20:00 pm Th   Friend Center   110   Enrolled:25 Limit:25 Closed
22992 P01A 07:30:00 pm - 08:20:00 pm Th   Sherrerd Hall   001   Enrolled:27 Limit:30
22911 P02 01:30:00 pm - 02:20:00 pm F   Friend Center   112   Enrolled:22 Limit:28
22984 P03 02:30:00 pm - 03:20:00 pm F   Friend Center   112   Enrolled:22 Limit:28
23015 P03A 02:30:00 pm - 03:20:00 pm F   Friend Center   009   Enrolled:23 Limit:28