### University of Kentucky

# MA 721: Topics in Numerical Analysis: Deep Learning

### Spring 2019

### MWF 1:00 pm - 1:50 pm, CB 343

## Instructor

Dr. Qiang Ye

Office: 735 Patterson Office Tower

Phone:257-4653

Email: qye3 "at" uky . edu

Office Hours: MWF 2:00-3:00 pm

## Textbook

There will be no required text, but the following books will be good sources of references:
## Syllabus

In this course, we study a widely applicable class of machine learning methods called deep learning. We will cover the following topics:
- Deep Feedforward Neural Networks.
- Convolutional Neural Networks
- Recurrent Neural Networks
- Deep Generative Models for Unsupervised Learning

Selected materials from optimization, linear algebra, and probability theory/information theory will be covered.
## Prerequisites

Familiarity with multivariate calculus, linear algebra and numerical methods will be assumed. Programming in Python will be required.
## Grading

The course grade will be based on programming projects (60%) and an in-class presentation (40%).
## Some references and links

Below are some links and books on numerical linear algebra, optimization, and machine learning that may be helpful.