Spring 2025
INFO 251 001 - LEC 001
Applied Machine Learning
Joshua Evan Blumenstock
Class #:30489
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
School of Information
Current Enrollment
Total Open Seats:
7
Enrolled: 93
Waitlisted: 0
Capacity: 100
Waitlist Max: 70
Open Reserved Seats:
87 reserved for Information Management and Systems: Masters & PhD Students
Hours & Workload
3 hours of instructor presentation of course materials per week, 8 hours of outside work hours per week, and 1 hours of the exchange of opinions or questions on course material per week.
Course Catalog Description
Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.
Rules & Requirements
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Reserved Seating For This Term
Current Enrollment
Open Reserved Seats:
87 reserved for Information Management and Systems: Masters & PhD Students
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials