University of Kentucky

MA 721: Topics in Numerical Analysis: Deep Learning

Fall 2017

MWF 1:00 pm - 1:50 pm, CB 347


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

     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. The following topics will be covered.      We will begin with an overview of numerical optimization and probability and information theory. Linear algebra is another area of math that will be used substantially. We will not devote time to cover them as most of the class have taken MA/CS 522 or 622 before. Those who have not taken a numerical linear algebra class are encouraged to consult one of reference books listed below. The most pertinent topics include linear least squares problems, singular value decompositions, and iterative methods such as the steepest descent and conjugate gradient algorithms.

Prerequisites

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

Computing

     We will use Python based toolboxs such as Keras/Theano/Tensorflow. You may choose to use any of them. Below are some links for setting up Keras/Theano/Tensorflow on Windows 10.