The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.
5709. Machine Learning for Data Science
3.00 credits
Prerequisites: GRAD 5100; Open to graduate students in the M.S. in Data Science program. Recommended preparation: Python programming, multivariable calculus, linear algebra, introductory statistics.
Grading Basis: Graded
An introduction to the techniques of machine learning, including models for both supervised and unsupervised learning, and related optimization techniques, covering topics such as regression, neural networks, clustering, model evaluation and selection, and implementation of learning algorithms from first principles.
Last Refreshed: 26-APR-24 05.20.16.357506 AM
| Term | Class Number | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Enrollment | Location | Credits | Grading Basis | Notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1248 4924 1 001 | Fall 2024 | 4924 | Storrs | In Person | Johnson, Joseph | 001 | Reg | TuTh 2:00pm‑3:15pm |
1/70 | ARJ 143 | 3.00 | Graded | |
| 1248 13984 1 002 | Fall 2024 | 13984 | Storrs | Online Asynchronous | Johnson, Joseph | 002 | Reg | 2/30 | No Room Required - Online | 3.00 | Graded |