Identifier
COMS 6730
Professor(s)
Last Updated: Spring 2025
- Credits and contact hours: 3 credits
- Textbook, title, author, and year: None required
- Other supplemental materials: The reading materials will primarily consist of lecture slides and recent research papers. A list of research articles will be provided within the first two weeks of the course.
Specific course information
- Brief description of the content of the course: Advanced topics in machine learning.
- Required, elective, or selected elective? Selected elective
Course Learning Objectives
Upon completing this course, students will be able to do the following:
- Understanding of the fundamental concepts in deep learning
- Ability to decide the appropriateness of various deep learning methods for a given task
- An understanding of how deep learning methods work and the principles behind their design, underlying assumptions, and limitations
- Ability to apply deep learning methods to data and to evaluate their performance
- Familiarity with deep learning methods to data and to evaluate their performance
- Familiarity with some current applications of deep learning
- Ability to communicate effectively about deep learning problems algorithms, implementations, and their experimental evaluation