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

Prerequisites: MATH 53 and MATH 54; and COMPSCI 70 or consent of instructor.

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

Formats:
Summer: 6.0 hours of lecture and 2.0 hours of discussion per week
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

Grading basis: letter

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

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Class Schedule (Spring 2025):
CS 189/289A – MoWe 18:30-19:59, Wheeler 150 – Jonathan Shewchuk

Class homepage on inst.eecs

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Department Notes: As of Fall 2019, CS289 has a final exam and no project.

Related Areas:

• Artificial Intelligence (AI)