Spring 2020
INFO 251 001 - LEC 001
Applied Machine Learning
Current Enrollment
Total Open Seats:
0
Enrolled:
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No Reserved Seats
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.
Other classes by Joshua Evan Blumenstock
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.
Class Notes
Prerequisites for this course are INFO 206B, 271B, or equivalent college-level course in computer science in Python or equivalent graduate-level coursework in statistics or econometrics per instructor's discretion.
Rules & Requirements
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats