Course Schedule - Fall Semester 2023

     

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 647 901 (CRN: 14674)

DEEP LEARNING

Long Title: DEEP LEARNING
Department: Computer Science
Instructor: Barman, Arko
Meeting:  (21-AUG-2023 - 1-DEC-2023) 
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
Section Max Enrollment: 35
Section Enrolled: 20
Enrollment data as of: 14-JUN-2024 3:53PM
 
Additional Fees: None
 
Final Exam: GR Course-Dept Schedules Exam
 
Description: In this course, students will learn the fundamentals of neural networks and deep learning along with their applications in several domains, such as computer vision and natural language processing. 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. Recommended Prerequisite(s): 1. Proficiency in Python 3. 2. Familiarity with fundamental concepts of calculus, including partial derivatives, chain rule, total derivatives, derivatives and partial derivatives of vectors and matrices. 3. Familiarity with fundamental concepts of probability and statistics, including probability distributions, density functions, computing probabilities, expectation, variance, multivariate distributions, random variables and multivariate random variables. 4. Familiarity with fundamental concepts of linear algebra, such as inner products, vector spaces, vector and matrix norms, rank of a matrix, positive definite matrices, and matrix factorization, e.g., spectral decomposition and singular value decomposition. 5. Familiarity with fundamental concepts of machine learning and optimization theory, such as loss functions, gradient descent, maximum likelihood estimation, MAP estimation, and principal component analysis. Mutually Exclusive: Cannot register for COMP 647 if student has credit for COMP 646/ELEC 576.