2025 Fall
COMPSCI 189 001 - LEC 001
Introduction to Machine Learning
Narges Norouzi, Joseph E. Gonzalez
Class #:28915
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
0
Enrolled: 346
Waitlisted: 210
Capacity: 346
Waitlist Max: 250
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 Narges Norouzi
Other classes by Joseph E. Gonzalez
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.
Class Notes
* Time conflicts are NOT allowed.
* Lecture WILL be recorded for playback later.
* Lecture WILL be recorded for playback later.
Rules & Requirements
Requisites
- Undergraduate Students: College of Engineering declared majors or L&S Computer Science or Data Science BA
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
Reserved Seating For This Term
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials