Spring 2019
COMPSCI 189 106 - DIS 106
Introduction to Machine Learning
Vasilis Oikonomou
Class #:28082
Units: 4
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
30
Enrolled: 0
Waitlisted: 0
Capacity: 30
Waitlist Max: 20
No Reserved Seats
Hours & Workload
1 hours of the exchange of opinions or questions on course material per week, 3 hours of instructor presentation of course materials per week, and 8 hours of outside work hours per week.
Other classes by Vasilis Oikonomou
Course Catalog Description
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Rules & Requirements
Credit Restrictions
Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A.
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats