MSDS 634 - Deep Learning

Unit(s): 2

This course introduces students to a range of topics and concepts in deep learning including the foundation neural networks, most common neural network architectures such as MLP, convolutional neural networks, and recurrent neural networks to name a few. Advanced topics such as generative models, geometric deep learning and graph neural networks are also covered. Students learn practical aspects of deep learning and using pytroch for creation/training/inference of various networks. Intuition, mathematical notions, and the practical aspects are emphasized throughout the course to build a solid theoretical and practical foundation of deep learning.

Prerequisite: MSDS 630 with a minimum grade of C- and MSDS 689 with a minimum grade of C-
Restriction: Level Restricted to Graduate; Field of study restricted to Data Science Major.
College of Arts and Sciences