COMPSCI189
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COMPSCI 189 - Introduction to Machine Learning
Subject
COMPSCI
Course Number
189
Department
Course Level
Undergraduate
Course Title
Introduction to Machine Learning
Course 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.
Minimum Units
4
Maximum Units
4
Grading Basis
Default Letter Grade; P/NP Option
Instructors
Abbeel, Bartlett, Darrell, El Ghaoui, Jordan, Klein, Malik, Russell
American Cultures Requirement
No
Reading and Composition Requirement
None of the Reading and Composition Requirement
Prerequisites
MATH 53 and MATH 54; and COMPSCI 70 or consent of instructor.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses
-
Formats
Lecture, Discussion
Term
Fall and Spring
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Discussion Hours
1
Outside Work Hours
8
Term
Summer
Weeks
8 weeks
Weeks
8
Lecture Hours
6
Discussion Hours
2
Outside Work Hours
16