University of Kentucky

MA 721: Topics in Numerical Analysis: Deep Learning

Fall 2017

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


     Dr. Qiang Ye
     Office: 735 Patterson Office Tower
     Email: qye3 "at" uky . edu
     Office Hours:  MWF 2:00-3:00 pm


     Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016


     In this course, we study a widely applicable class of machine learning methods called deep learning. We will primarily be covering the following topics from Part II of the text:      Selected materials from Part I of the text will be reviewed. We plan to begin with an introduction to numerical optimization and probability theory. Linear algebra is another area of math that will be used substantially. However, we will not devote time to cover them as most of the class have taken MA/CS 522 or 622. Those who have not taken a numerical linear algebra class before are encouraged to consult Part I of the text and/or one of the references listed below. The most pertinent topics include conditioning, linear least squares problems, singular value decompositions, and the steepest descent and the conjugate gradient methods.


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


     The course grade will be based on two programming projects (40%), an in-class presentation (30%), and attendance (30%).

Course Materials


     We will use Python based toolboxs such as Keras, Theano, or Tensorflow. You may choose to use any of them. It is best to run them on the Linux platform. But if you use Windows, below are some links for setting up Keras/Theano/Tensorflow on Windows 10. You may also install these toolboxes using ANACONDA DISTRIBUTION. Download the distribution and try the following (Thanks to Liu Liu for these tips!)

Some references and links

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