Course Schedule - Fall Semester 2022

     

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 642 901 (CRN: 12600)

MACHINE LEARNING

Long Title: MACHINE LEARNING
Department: Computer Science
Instructor: Straach, Janell
Meeting:  (22-AUG-2022 - 2-DEC-2022) 
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Online
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
 
Restrictions:
Must be enrolled in one of the following Program(s):
Online Master of Data Science
Online Master Computer Science
Must be enrolled in one of the following Level(s):
Graduate
Prerequisites: COMP 682
Section Max Enrollment: 35
Section Enrolled: 24
Enrollment data as of: 3-MAY-2024 6:57AM
 
Additional Fees: None
 
Final Exam: GR Course-Dept Schedules Exam
 
Description: Machine learning is the process of automatically inferring a function from a set of data. In essence, machine learning techniques seek to automate the inductive learning process that humans do so well. Furthermore, the availability of large training sets combined with significant computing power has made machine learning an extremely important body of knowledge across a large range of application domains. A small sample of some of the application domains include robotics, medicine, speech/facial recognition, and driving autonomous vehicles. This course will focus on providing a foundational understanding of modern algorithms in machine learning, focusing on practical applications. Enrollment in online sections limited to students in the OMCS or OMDS programs. Enrollment in in person sections limited to students in the MCS or MDS programs. In order to enroll in an online section of this course, you are expected to have a working camera and microphone. During class sessions, you must be able to participate using your microphone and you are expected to have your camera on for the duration of the class so that you are visible to the instructor and other students in the class, just as you would be in an in-person class.