CSCI1420
Machine Learning
Fall 2025
How can artificial systems learn from examples and discover information buried in data? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised learning. Specific topics include empirical risk minimization, probably approximately correct learning, kernel methods, neural networks, maximum likelihood estimation, the expectation maximization algorithm, and principal component analysis. This course also aims to expose students to relevant ethical and societal considerations related to machine learning that may arise in practice. Please contact the instructor for information about the waitlist.
Instructor's Permission Required
| Instructor(s): | |
| Meets: | TTh 2:30pm-3:50pm Location TBD |
| Exam: | If an exam is scheduled for the final exam period, it will be held: |
| Max Seats: | 83 |
| CRN: | 18575 |
Spring 2026
As above
| Instructor(s): | |
| Meets: | TTh 2:30pm-3:50pm Location TBD |
| Exam: | If an exam is scheduled for the final exam period, it will be held: |
| Max Seats: | 200 |
| CRN: | 26702 |
