Spring 2022
COMPSCI 189 002 - LEC 002
Introduction to Machine Learning
Marvin M Zhang
Class #:32547
Units: 4
Instruction Mode:
In-Person Instruction
Time Conflict Enrollment Allowed
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
39
Enrolled: 161
Waitlisted: 0
Capacity: 200
Waitlist Max: 100
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
TUE, MAY 10TH
08:00 am - 11:00 am
Physics Building 1
Physics Building 2
Physics Building 3
Evans 6
Evans 4
Other classes by Marvin M Zhang
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
Lecture 002 is for UNDERGRADUATE DATA SCIENCE students only! All other students should enroll into Lecture 001.
For planning purposes, discussion sections marked with DS in the title (eg. 21DS) are associated with this lecture. You cannot attend discussion sections without a DS designat.. show more
For planning purposes, discussion sections marked with DS in the title (eg. 21DS) are associated with this lecture. You cannot attend discussion sections without a DS designat.. show more
Lecture 002 is for UNDERGRADUATE DATA SCIENCE students only! All other students should enroll into Lecture 001.
For planning purposes, discussion sections marked with DS in the title (eg. 21DS) are associated with this lecture. You cannot attend discussion sections without a DS designation.
Interested Data Science majors should enroll into Lecture 002 and DIS 99DS.
The waitlist for this lecture will process SEPARATELY from the waitlist for lecture 001.
Time conflicts for this version of the class will be allowed however NO alternate final exam will be offered! Students must make sure they do not exam conflicts - no accommodation will be made.
The lecture for this class will be webcast
Questions regarding this version of CS 189 should be directed to ds-enrollments@berkeley.edu. show less
For planning purposes, discussion sections marked with DS in the title (eg. 21DS) are associated with this lecture. You cannot attend discussion sections without a DS designation.
Interested Data Science majors should enroll into Lecture 002 and DIS 99DS.
The waitlist for this lecture will process SEPARATELY from the waitlist for lecture 001.
Time conflicts for this version of the class will be allowed however NO alternate final exam will be offered! Students must make sure they do not exam conflicts - no accommodation will be made.
The lecture for this class will be webcast
Questions regarding this version of CS 189 should be directed to ds-enrollments@berkeley.edu. show less
Rules & Requirements
Requisites
- Undergraduate Data Science Majors
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