2022 Fall
COMPSCI 189 001 - LEC 001
Introduction to Machine Learning
Jennifer Listgarten, Jitendra Malik
Class #:27623
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
47
Enrolled: 243
Waitlisted: 0
Capacity: 290
Waitlist Max: 100
Open Reserved Seats:
37 unreserved seats
10 reserved for Undergraduate Data Science Majors
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
TUE, DECEMBER 13TH
08:00 am - 11:00 am
Pimentel 1
Evans 55
Evans 70
Evans 71
Evans 75
Other classes by Jennifer Listgarten
Other classes by Jitendra Malik
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
* This class will NOT be webcast.
* Time conflicts are NOT allowed for this class.
* Data Science majors are allowed to enroll in this class.
* Time conflicts are NOT allowed for this class.
* Data Science majors are allowed to enroll in this class.
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
Current Enrollment
Open Reserved Seats:
37 unreserved seats
10 reserved for Undergraduate Data Science Majors
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials