Spring 2022
COMPSCI 189 999 - DIS 999
Introduction to Machine Learning
Jan 18, 2022 - May 06, 2022
12:00 am
Class #:22183
Units: 4
Instruction Mode:
In-Person Instruction
Time Conflict Enrollment Allowed
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
45
Enrolled: 586
Waitlisted: 0
Capacity: 631
Waitlist Max: 325
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.
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
Requisites
- Undergraduate Students - Excludes Visiting Students
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
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials