Introduction to Neural Networks
CMSC475/675, Fall 2011
Mon/Wed 2:30 – 3:45, SOUND101
Instructor: Professor Yun Peng
ITE 341, x5-3816, ypeng@umbc.edu
Office Hour: MW 1:00 – 2:00PM or by appointment
The most important thing you should
remember: URL for the course
http://www.csee.umbc.edu/~ypeng/F11NN/NN.html
• All information important to the class is/will be
posted on this web site.
• No hard copy handouts will be given
• Read class announcement on the web
• Contact me by email outside the office hours
Course Overview
• Texts:
Elements of Artificial Neural Networks, by Mehrotra,
Mohan, and Ranka, MIT Press, 1997.
• Prerequisites:
– CMSC341 (or experience in other programming languages)
– Knowledge in algorithms and AI is helpful
– Some knowledge in linear algebra/matrix and differential
equations
– Biology/psychology/neural science/cognitive science not
required
Course Overview
• Grading
– 2 projects
– 2 exam
– Project 3
20% each
30% each (additional questions for 675
students)
20% for 675 students only
• Academic dishonesty
– UMBC Student Honor Code (from Student Handbook)
“… Cheating, fabrication, plagiarism, and helping others to
commit these acts are all forms of academic dishonesty, ...
Academic misconduct could result in disciplinary action that
may include, but is not limited to, suspension or dismissal…”
Course Overview
• Highlights
– Central theme borrowed from networks of neurons in
animal nerve systems
– An alternative approach to computational models for
problem solving (in contrast to algorithmic, Von
Neumann approach)
– Multi-disciplinary
• Computer science
• Mathematics/statistics/physics
• Neural science (medicine, zoology, biology)
• Cognitive science/psychology
• Engineering
Course Overview
• Emphasis of this course:
– Computational aspects of NN
• Network structures
• Computation and learning methods
– Not on modeling of
• Biological nerve systems and their functions
• Cognitive behaviors
– Limited mathematical treatment
– Limited coverage on applications
Course Overview
• Main topics:
– Basics of NN
• Neurons and inter-neuron connections
• Differences with conventional Von Neumann
computing
– Major NN models
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•
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Adaline
Perceptron
Multi-layer feed forward nets
Recurrent nets
Hopfield nets and other thermodynamic models
Self organizing nets
Course Overview
– Learning
•
•
•
•
Hebbian rule
Backpropogation (error driven and gradient descent)
Supervised and unsupervised learning
Reinforcement learning
– Applications
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•
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Function approximation
Pattern analysis
Prediction
Optimization
CMSC 475/675
All the best !!!!
10/5/2009
•
Exam 1: Oct 12 (next Monday)
–
–
•
Project 1
–
–
•
Review for exam 1 is posted
Revised slides for Ch 4 is posted
Due Oct 19 (by the end of the class of that day)
What to submit
• Source code
• Running result
• Project report
Authors’ Errant
–
http://www.cis.syr.edu/~mohan/html/book.html
10/19/2009
• Exam1
5 graduate students (120 points)
min max average
79 116 96
6 undergrad students (100 points)
min max average
52 88 75.6
10/26/2009
• Project 2
– Due Monday, 11/23
– Experimenting SOM
10/26/2009
• Project 2
– Due Monday, 11/23
• Project 3 (for 675 students only)
– Experiment with different NN models for auto
associative memory
• Discrete Hopfield model (DHM)
• Back propagation learning + recurrent recall (BDRR)
– Test their storage capacities and other properties (e.g.,
pattern correction, cross-talk)
– Project description will be uploaded to the class website
by tomorrow
– Due May 15, the last day of class (also the day for
final exam)
11/16/2009
• Project 2
– Due Nov. 23
• Project 3 (for 675 students only)
– Due Dec 14 (the last day of class)
– Proposal due 11/18
• Final exam
– Dec 14
– Material since midterm exam
– A review on Dec 9 (Wed.)
• No class on Wed. Nov. 25
5/8/2006
• Project 3 (for 675 students only)
– Due May 15
• No class Wed. 5/10
• Final exam
– May 15
– Material since midterm exam