CS 4143 - Deep Learning

Instructor: Andrew L. Mackey
Meeting Times: Tuesdays/Thursdays at 9:30 - 10:45 pm
Location: Baldor 147
Lab Assistant: Kyle Kelly
Lab Times: Fridays at 2:00 pm - 4:00 pm

Course Overview

This course focuses on advancements to recent advancements to neural network architectures. It examines deep neural networks and their applications to the fields of artificial intelligence, natural language processing, machine learning, and computer graphics and vision.

Prerequisites

Students are required to have a prior background in artificial intelligence or machine learning, probability theory, matrix algebra, and calculus.

Outline

The follow list contains courses that I have taught within the past few years.

Week Topic Notes
1 Course Introduction Complete the Tensorflow Lab
2 Deep Learning Fundamentals Complete the PyTorch Lab
3 Deep Learning Fundamentals (cont.)  
4 Convolutional Neural Networks Complete the Image Classification Lab
5 Convolutional Neural Networks (cont.)  
6 Recurrent Neural Networks Complete the RNN Lab
7 Recurrent Neural Networks (cont.)  
8 Attention and Transformers  
9 Generative Models  
10 Generative Models (cont.)  
11 Reinforcement Learning Complete the GANS Lab
12 Reinforcement Learning (cont.)  
13 Advanced Topics in Deep Learning  
14 Advanced Topics in Deep Learning (cont.)  
15 Final Project Presentations  
16 Review and Final Examination  

Major Course Topics

Assigned Reading

No reading material has been assigned. However, the following textbooks are optional and may supplement the material in this class:

Final Project

All students will be required to complete a final project. Students will go above and beyond existing current knowledge to attempt a novel research project. Students will investigate new architectures to solve an existing problem, adapt some methodology from a different problem to solve an existing problem, or define and solve a new problem of interest.

Resources

The following resources are available for students to use.

Lab Work

Review the course website for more information