University of Kentucky
MA 421G: Mathematical Introduction to Deep Learning
Spring 2024
MWF 11:00 am - 11:50 am, CB 341
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 book freely available online will be a good source of references:
- Deep Learning by
Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016
Syllabus
In this course, we study a widely applicable class of machine learning methods called deep learning. We will cover the following topics:
- Introduction to Machine Learning.
- Deep Feedforward Neural Networks.
- Optimization and Regularization.
- Convolutional Neural Networks
- Recurrent Neural Networks
- Unsupervised Learning
Selected materials from optimization, linear algebra, and probability theory will be covered.
Prerequisites
MA/STA 320 (or STA 524), MA/CS 321, and MA 322, or consent of instructor.
Python programming will be used.
Course delivery
The course
will be in-person. Canvas will be the main platform. You will
find all the information about teaching, assignments, and other
online resources through your canvas webpage. The information
here is tentative and is subject to changes.
Some references and links
Below are some links and books on numerical linear algebra, optimization, and machine learning that may be helpful.