CS747
Deep Learning(Fall 2023)

Meeting Time and Location:
T 4:30 pm - 7:10 pm ,ONLINE BLACKBOARD COLLABORATE

Instructor: Prof. Daniel Barbará.
Email: dbarbara (at) gmu (dot) edu
Office: Eng. Bldg 4420
Office hour: by appointment

Graduate TA: TBA

Course Home Page
CS747

Overview
This course provides an introduction to the fundamental concepts in Deep Learning

Multilayer Perceptrons and algorithms to train them. Hidden units.
Backpropagation, Gradient descent and Stochastic Gradient Descent. .
Regularization for Deep Learning
Convolutional methods (CNNs).
Recurrent and recursive nets (RNNs).
Practical aspects and evaluation of Deep Learning
Autoencoders.
Generative Adversarial Networks (GANs).

Prerequisites: CS 580, or CS 584, CS 688.

Textbook

The Science of Deep Learning by Iddo Drori. Cambridge University Press.

Dive into Deep Learning by Zhang et al (https://d2l.ai/)

Deep Learning with Python, 2nd edition by F. Chollet

Class Attendance
Required. Please arrive on time. I expect to start at 4:30pm sharp; Please participate in class! Ask questions if there is something you don't understand.

Grading Policies

End-of-semester numeric scores will be weighted as follows (tentative plan):

50% Assignments
25% Final project
10% Participation (measured by submission of activities reports)
15% Theory assignments
Late Policy
No late assignments will be accepted.
No smartphones, LAPTOPS, TABLETS, or recorders allowed in class. Lectures cannot be recorded without special permission
Honor Code
You are expected to abide by the honor code. Please refer to GMU Honor Code. Any violation of the honor code will result in a zero of the assignment/exam, and may result in an F for the class.
School Calendar

Disability Services

CS747 Deep Learning(Fall 2023)

CS747
Deep Learning(Fall 2023)