Spring 2025
COMPSCI 189 001 - LEC 001
Introduction to Machine Learning
Jonathan Shewchuk
Class #:28904
Units: 4
Instruction Mode:
In-Person Instruction
Time Conflict Enrollment Allowed
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
-2
Enrolled: 705
Waitlisted: 0
Capacity: 703
Waitlist Max: 500
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.
Final Exam
FRI, MAY 16TH
03:00 pm - 06:00 pm
Pimentel 1
Valley Life Sciences 2050
Stanley 105
Evans 10
Other classes by Jonathan Shewchuk
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 allowed but NO alternate final exam will be offered.
* Lecture WILL be recorded for playback later.
* NO alternate final exam will be offered
* A limited number of seats have been set aside for Data Science majors.
* Lecture WILL be recorded for playback later.
* NO alternate final exam will be offered
* A limited number of seats have been set aside for Data Science majors.
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
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