CPSC 636-600 Neural Networks
Spring 2012

NEWS: 4/24/12, 12:11PM (Tue)
Read-Only Bulletin Board.: 1/16/15, 11:09AM (Fri)

Page last modified: 1/16/15, 11:07AM Friday.

General Information Resources Weekly Schedule Lecture Notes Example Code Read-Only Board

I. General Information


Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 979-845-5466
Office hours: MF 2pm-3pm.


There will be no TA for this class.
Office hours:


Math 304 (linear algebra) and 308 (differential equations) or approval of instructor. (Actually, if you are mildly familiar with linear algebra and have taken calculus, you should be fine.)

Prior programming experience is not a prerequisite, but there will be programming assignments.


TR 9:35am-10:50am HRBB 126


Basic concepts in neural computing; functional equivalence and convergence properties of neural network models; associative memory models; associative, competitive and adaptive resonance models of adaptation and learning; selective applications of neural networks to vision, speech, motor control and planning; neural network modeling environments.


The official textbook for this class will be: However, a lot of overlapping material appear in the older edition: so this could be a good, cheaper alternative.

Other books: see slide01.pdf.

Computer Accounts and Usage:

  1. Computer accounts: if you do not have a unix account, ask for one on the CS web page.

Topics to be covered:

See the Weekly Schedule section for more details.


  1. Exams: 40% (midterm: 20%, final: 20%)
  2. Assignments: 60% (5 written+programming assignments, 12% each)
Grading will be on the absolute scale. The cutoff for an `A' will be 90% of total score, 80% for a `B', 70% for a `C', 60% for a `D', and below 60% for an 'F'.

If you are absent without any prior notification to the instructor, your class participation score will be set to 0% at the very first occurrence, except for unforseen emergencies.

Academic Integrity Statement:

AGGIE HONOR CODE: An Aggie does not lie, cheat, or steal or tolerate those who do.

Upon accepting admission to Texas A&M University, a student immediately assumes a commitment to uphold the Honor Code, to accept responsibility for learning, and to follow the philosophy and rules of the Honor System. Students will be required to state their commitment on examinations, research papers, and other academic work. Ignorance of the rules does not exclude any member of the TAMU community from the requirements or the processes of the Honor System.

For additional information please visit: http://www.tamu.edu/aggiehonor/

Local Course Policy:

Students with Disabilities:

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Life, Services for Students with Disabilities, in Cain Hall or call 845-1637.

II. Resources

  1. Matlab code for the examples in the text book (2nd edition): Download the ZIP archive.
  2. Linear algebra review (by Eero Simoncelli)
  3. Matlab Primer by Kermit Sigmon, University of Florida
  4. GNU Octave http://www.octave.org (compatible with Matlab)
  5. My general resources page
  6. 625/689 Reading List

III. Weekly Schedule and Class Notes

Notices and Dues
1 1/17 Introduction Chap 1 (Intro chapter, 3rd ed)     slide01.pdf
1 1/19 Introduction Chap 1 (Intro chapter, 3rd ed)     slide01.pdf
2 1/24 Learning process Chap 2 (Intro chapter sections 8, 9)     slide02.pdf
2 1/26 Learning process Chap 2 (Intro chapter sections 8, 9)     slide02.pdf
3 1/31 Learning process Chap 2 (Intro chapter sections 8, 9) Homework 1 assigned   slide02.pdf
3 2/2 Single-layer perceptrons Chap 3 (Chap 1, Chap 3)     slide03.pdf
4 2/7 Single-layer perceptrons Chap 3 (Chap 1, Chap 3)     slide03.pdf
4 2/9 Multi-layer perceptrions Chap 4 Homework 2 assigned (2/9) Homework 1 due slide04.pdf
5 2/14 Multi-layer perceptrions Chap 4     slide04.pdf
5 2/16 Multi-layer perceptrions Chap 4     slide04-suppl.pdf
6 2/21 Radial-basis functions Chap 5   Homework 2 due slide05.pdf
6 2/23 Guest lecture (Tim Mann)     NIH review panel  
7 2/28 Radial-basis functions Chap 5     slide05.pdf
7 3/1 Midterm exam (in class)        
8 3/6 Special topic Biologically inspired models Homework 3 assigned   slide06.pdf
8 3/8 Special topic Biologically inspired models     slide06.pdf
9 3/13 Spring Break No class      
9 3/15 Spring Break No class      
10 3/20 Special topic Intrinsic semantics through sensorimotor learning     slide06-sida.pdf
10 3/22 Self-organizing maps Chap 9   Homework 3 due slide07.pdf
11 3/27 Self-organizing maps Chap 9     slide07.pdf
11 3/29 Neurodynamics Chap 14 (3rd ed. Chap 13) Homework 4 assigned   slide08.pdf
12 4/3 No lecture See Prof. Jay McLelland's online lecture      
12 4/5 Support-vector machines Chap 7     slide09.pdf
13 4/10 Principal component analysis/Info theory Chap 8, 10     slide10.pdf
13 4/12 Information-theoretic models Chap 10     slide11.pdf
14 4/17 Information-theoretic models Chap 10, ICA     slide11.pdf
14 4/19 Information-theoretic models Sarma and Choe (2006), Lee and Choe (2003), Sarma (2003); Langlois and Garrouste (1997)   Homework 4 due, in class slide12.pdf
15 4/24 Neuroevolution       slide14.pdf
15 4/26 Exam 2 (in class)      
16 5/1 Redefined day: No class Liquid state machine (Reservoir computing; Wolfgang Maass); Deep Learning (Geoff Hinton; Juergen Schmidhuber)      

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