Course Schedule and Catalog
Course Schedule - Fall Semester 2024
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 605 001 (CRN: 17513)
GR SEM ON LEARNING THEORY
Long Title: GRADUATE SEMINAR IN LEARNING THEORY
Department: Computer Science
Instructor: Aliakbarpour, Maryam
Meeting: 4:00PM - 5:15PM W (26-AUG-2024 - 6-DEC-2024)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Seminar
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 1 OR 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 20
Section Enrolled: 2
Enrollment data as of: 26-DEC-2024 2:05PM
Final Exam: GR Course-Dept Schedules Exam
Description: This course offers an in-depth exploration of the mathematical and computational foundations that underpin the algorithms and models used in machine learning. It is designed to delve into the theoretical aspects of machine learning, covering topics such as statistical learning theory, complexity theory, algorithmic efficiency, and differential privacy. The seminar aims to equip students with a robust understanding of how and why machine learning algorithms work, enabling them to design and implement more effective and efficient models.
Students have the option of registering for a 3-credit hour project, which provides the opportunity to engage with the topic through a project for those seeking a more hands-on experience in the course. Recommended Prerequisite(s): machine learning or an algorithm course is strongly recommended.