COMPSCI189
Download as PDF
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
Repeat Rules
Course is not repeatable for credit.
Grading Basis
Default Letter Grade; P/NP Option
Instructors
Abbeel, Bartlett, Darrell, El Ghaoui, Jordan, Klein, Malik, Russell
Prerequisites
MATH 53 and MATH 54; and COMPSCI 70 or consent of instructor.
Credit Restriction Courses
-
Credit Restrictions
Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A.
Term
Fall and Spring
Lecture Hours
3
Discussion Hours
1
Term
Summer
Lecture Hours
6
Discussion Hours
2