CS 747
Deep Learning

Time/Location: Thurday 4:30-7:10pm, Planetary Hall 124  
Instructor: Jana Kosecka
Office hours:  Wednesday 3-4pm, ENGR 444
TA office hours:  tbd
Contact: Office 4444 Engineering Building
e-mail: kosecka@gmu.edu, 3-1876
Course Communications: Piazza , Canvas

This course will cover an introduction to neural networks and deep learning. We will cover multi-layer neural networks, convolutional neural networks, recurrent neural networks, transformers, generative neural networks and deep reinforcement learning. We will discussed representative models and techniques for image classification, image and text generation, perception and action.The class will consist of programming assignments in Python (PyTorch), paper review/presentations and final project.

The course will comprise of lectures by the instructor, homeworks, paper review/presentations and final project.

Prerequisites:

CS 688, strong programming experience and willingness to participate in discussions
Students taking the class should be comfortable with linear algebra, calculus and probability

Recommended Textbooks:

Dive into Deep Learning" (online available: https://d2l.ai/) Deep Learning by Goodfellow et. link here
Deep Learning with Python, 2nd edition by F. Chollet

Grading:

Assignments: 60%
Exam: 20%
Final project: 20%
Participation: 10% (measured by submission of activities reports)

Late policy:

Each student will have a 2 day late submission budget, which could be used towards late submission on the homeworks.

Grading scale:

A   92%
A-   90%
B+   87%
B   83%
B-   80%
C+   77%
C   72%
C-   67%
D   60%
F   < 60

Outline of topics:

  • Machine learning refresher, Python/numpy, linear classifiers
  • Neural Networks, Backpropagation, Computational Graphs
  • Optimization, common loss functions, training neural networks
  • Convolutional neural networks, object detection, dense prediction
  • Autoencoders, Variational Autoencoders Generative Adversarial Networks
  • Recurrent Neural Networks, Attention, Transformers
  • Metric and unsupervised learning, adversarial examples
  • Deep Reinforcement Learning
  • Applications: Computer Vision, Robotics, Natural Language Processing and others

    • CS department Honor Code can be found here

    Disability Statement

    If you have a documented learning disability or other condition that may affect academic performance you should:
  • Make sure this documentation is on file with the Office of Disability Services (SUB I, Rm. 222; 993-2474; www.gmu.edu/student/drc) to determine the accommodations
  • Talk with me to discuss your accommodation needs