Deep Learning

3 Credits · Level: Graduate, Undergraduate

Prerequisites: MATH 5051 AND MATH 5200 or Equivalents. Description: This MS level course is an introduction to Neural Networks and Deep Learning. This is a first course on the mathematical foundations of neural networks with practical applications in R or Python. This course will begin with review of preliminaries in linear algebra, machine learning, and numerical computation. Then this course will move on to several neural network architectures such as feed forward networks, recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM) networks, autoencoders, generative adversarial networks (GAN), and transformers. Students will learn the mathematics of these architectures, their real-world applications, optimization techniques, and implementations in R or Python, depending on the student's choice.