Catalog Description: This course will provide familiarity with algorithms and probabilistic models that arise in various computational biology applications, such as suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, hidden Markov models, gene finding, motif finding, linear/logistic regression, random forests, convolutional neural networks, genome-wide association studies, pathogenicity prediction, and sequence-to-epigenome prediction.

Units: 4

Also Offered As: CMPBIO C176, COMPSCI C176

Student Learning Outcomes: Understand the basic elements of molecular, cell, and evolutionary biology. Understand various data structures and algorithms that arise in computational biology. Understand the key probabilistic and machine learning models used in computational biology applications.

Prerequisites: COMPSCI 70 and COMPSCI 170, MATH 54 or EECS 16A or an equivalent linear algebra course

Credit Restrictions: Students will receive no credit for COMPSCI C176 after completing COMPSCI 176.

Formats:
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: Default Letter Grade; P/NP Option

Final Exam Status: Yes

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Class Schedule (Spring 2026):
CS C176 – TuTh 15:30-16:59, Cory 247 – Allon Wagner

Class Notes
- Time conflicts are allowed.

- Section 101 is no longer available. All students must enroll in section 102.

Links:

• EECS Course Archives (CalNet authentication required)
• CS enrollment policies
• EE enrollment policies
• HKN course maps