CSE 251A. Machine Learning: Learning Algorithms (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/CSE.html#cse251a
Description
(Formerly CSE 250B.) Algorithms for supervised and unsupervised learning from data. Content may include maximum likelihood; log-linear models, including logistic regression and conditional random fields; nearest neighbor methods; kernel methods; decision trees; ensemble methods; optimization algorithms; topic models; neural networks; and backpropagation. Renumbered from CSE 250B. Students may not receive credit for CSE 251A and CSE 250B. Course may be coscheduled with CSE 151A. Prerequisites: graduate standing or consent of instructor. Restricted to students in CS75, CS76, CS78, and CS89. Other graduate students will be allowed as space permits. Recommended preparation: some experience with probability and statistics recommended.
Prerequisite courses
CSE 251A has no prerequisite courses.
Successor courses
CSE 251A is a prerequisite of the following 1 courses: