| Statistical Machine Learning - 11434 - CS 57800 - LE1 |
||||
|---|---|---|---|---|
|
Associated Term: Spring 2018
Levels: Undergraduate, Graduate, Professional West Lafayette Campus Lecture Schedule Type Learning Outcomes: During the course, students will: 1) Learn about different supervised and unsupervised problems, and their related algorithms. 2) Implement some of those algorithms. 3) Learn the theory behind some algorithms, e.g., geometrical aspects and generalization. 4) Learn algorithm-independent principles, e.g., cross-validation, bias-variance tradeoff. Required Materials: See website Technical Requirements: This class requires some mathematical background. It's not a math class, however you should be comfortable with linear algebra, calculus, statistics and probability. Programming knowledge is also required. View Catalog Entry
| ||||
