CS 189. Introduction to Machine Learning

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.

Units: 4.0

Prerequisites: Mathematics 53 and 54; Computer Science 70 or consent of instructor.

Credit Restrictions: Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A.

Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week
Summer: 6.0 hours of lecture and 2.0 hours of discussion per week

Grading basis: letter

Final exam status: Written final exam conducted during the scheduled final exam period

Class Schedule (Fall 2018):
TuTh 9:30AM - 10:59AM, Li Ka Shing 245 – Benjamin Recht, Moritz Hardt, Stella Yu

Spring 2017 class homepage on bCourses

Class homepage on inst.eecs

General Catalog listing

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