CS 395 Independent Work in Computer Science (Applied Deep Learning)

University of Kentucky
Department of Computer Science
CS 395 Independent Work in Computer Science (Applied Deep Learning)
 
 
1.  Course Number/Name:  CS 395, Independent Work in Computer Science (Applied Deep Learning)
 
2.  Credits and Contact Hours:  2 credits, 1 contact hours
 
3.   Instructor:  assigned by department
 
4.   Textbook:   none 
 
5.   a.  Catalog Description: A course for computer science majors only. A problem, approved by the chairperson of the
            department, provides and opportunity for individual research and study. May be repeated to a maximum of six credits.
 
b.  Prerequisites/Co-requisites:  Major and a standing of 3.0 in the department and consent of instructor.
 
c.  Required course: elective 
 
6.  a.   Outcomes of InstructionAfter completing the course, the student will be able to:

1.    Describe the fundamental methods used to train deep-network architectures.
2.    Describe the methods necessary for training deep-networks for regression problems.
3.    Describe recent innovations in deep learning, as applied to computer vision problems.
4.    Document findings in the format of an academic publication.

b.   Contributions to Student Outcomes from Criterion 3
           

Outcome

a

b

c

d

e

f

g

h

i

j

k

CS 395

3

3

3

 

 

2

 

1

3

3

 

3- Strongly supported   2 – Supported   1 – Minimally supported
 
7.   List of Topics Covered:

1.    Software libraries for deep convolutional neural networks
2.    Optimization methods for deep convolutional neural networks.
3.    Software development to solve applied problems using deep convolutional neural networks