Course Schedule and Catalog
Course Schedule - Fall Semester 2026
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
COMP 282 001 (CRN: 16130)
COMPUTATIONAL OPTIMIZ. FOR AI
Long Title: COMPUTATIONAL OPTIMIZATION FOR AI
Department: Computer Science
Instructor: Kyrillidis, Tasos
Meeting: 11:00AM - 11:50AM MWF (24-AUG-2026 - 4-DEC-2026)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Undergraduate Professional
Visiting Undergraduate
Undergraduate
Prerequisites: (MATH 102 OR MATH 106 OR MATH 212) AND COMP 182 AND (MATH 355 OR MATH 354 OR MATH 221 OR CMOR 302 OR CMOR 303)
Section Max Enrollment: 75
Section Enrolled: 28
Enrollment data as of: 26-APR-2026 2:31PM
Final Exam: Scheduled Final Exam-OTR Room
Description: This course provides the mathematical and computational foundations necessary for understanding mod-ern AI/ML systems, with a focus on optimization and computational perspectives. The course interweaves three main threads: (1) computational linear algebra through the lens of optimization problems, (2) multi-variate calculus and optimization concepts essential for understanding learning algorithms, and (3) practical implementation using Python’s scientific computing ecosystem. Students will learn to implement and analyze fundamental algorithms for machine learning, developing both theoretical understanding and practical coding skills. The course emphasizes computational efficiency, algorithm implementation, and the connections between mathematical theory and practical AI applications. This class is intended to prepare students for upper-level AI classes.