University of Kentucky
MA 721: Selected Topics in Numerical Analysis: Deep
Learning Algorithms
Fall 2025
MWF 1:00 pm - 1:50 pm, Chemistry-Physics Bldg Rm.183
Instructor
Dr. Qiang Ye
Office: 735 Patterson Office Tower
Phone:257-4653
Email: qye3 "at" uky . edu
Office Hours: MWF 3:00-4:00
pm
Textbook
There will be no required text, but the following book will be a good source of references:
- Dive into Deep Learning by by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J.
Smola, Cambridge University Press, 2023.
Course Announcement
Here is a course announcement where most information can be found.
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 and Linear Models.
- Fully Connected Neural Networks
- Optimization and Regularization
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs), Transformer, and Language Models
- Graph Convolutional Networks (GCNs)
- Generative Models and Unsupervised Learning
- Principal Component Analysis (PCA)
- Autoencoder, and Variational Autoencoder (VAE)
- Generative Adversarial Networks (GANs)
- Stochastic Differential Equations and Diffusion Models
Selected materials from optimization,
linear algebra, and probability/information theory will be
reviewed.
Prerequisites
Familiarity with multivariate calculus, linear algebra, numerical methods, and programming in Python.
Some references and links
Below are some links and books on numerical linear algebra, optimization, and machine learning that may be helpful.