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Course Offerings

Course Evaluation Results

Course Details

Fall 2018-2019
* COS 324   Graded A-F, P/D/F, Audit

Introduction to Machine Learning

Ryan P. Adams

Gives broad introduction to different learning paradigms and algorithms, providing a foundation for further study or independent work in artificial intelligence and data science. Supervised learning: regression, classification, multiclass categorization, deep learning, ensemble methods. Unsupervised learning: linear factor analysis, PCA, clustering, probabilistic modeling. Reinforcement learning: sequential decision making, exploration/exploitation tradeoffs, optimal planning in a markov decision process.


Sample reading list:
See instructor for complete list

Reading/Writing assignments:
Problem sets and programming exercises. Programming assignments will use python.

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

Prerequisites and Restrictions:
MAT 103, MAT 104, MAT 202 Linear Algebra (or equivalents). COS 226: Algorithms and Data Structures. Also, elementary knowledge of partial derivatives and gradients (as the level of Khan Academy's module on this topic)..

Other information:
This newly-designed course will be offered each term, and strongly recommended as a first course in machine learning for COS majors.

Website:  http://www.cs.princeton.edu/courses/archive/fall2018/cos324

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
21305 L01 11:00:00 am - 12:20:00 pm T Th   Computer Science Building   104   Enrolled:42 Limit:125
21306 P01 07:30:00 pm - 08:20:00 pm Th   Friend Center   111   Enrolled:17 Limit:28
23221 P01A 07:30:00 pm - 08:20:00 pm Th        Enrolled:0 Limit:0 Canceled
21307 P02 01:30:00 pm - 02:20:00 pm F   Friend Center   110   Enrolled:10 Limit:25
21308 P03 07:30:00 pm - 08:20:00 pm T   Friend Center   110   Enrolled:13 Limit:28
23222 P03A 02:30:00 pm - 03:20:00 pm F        Enrolled:0 Limit:0 Canceled