Artificial Neural Networks
Graduate level
Graduate level
Fall 2024
Lectures: Monday 14-16 - Wednesday 10-12
Instructor: Dr. A. Abdollahpouri
Course Description
An
introduction to artificial neural networks. Topics include a survey of
natural neural network models, perceptrons and their limitations,
multi-layer networks and back propagation, unsupervised competitive
learning, associative networks, adaptive resonance theory, applications
of connectionist models of computing to various domains, including
pattern recognition, databases, etc.
Textbooks
Kevin Gurney, An
Introduction
to Neural Networks
Laurene
Fausette, Fundamentals of neural networks, architecture, algorithms and
application, Prentice Hall,
M.B.
Menhaj:
Neural
Networks (in
Persian)
Lecture notes
| Physiological Aspects | Lecture0 |
| Introduction | Lecture1 |
| Perceptron, MLP and Backpropagation | Lecture2 |
| Associative Neural Networks | Lecture3 |
| Competitive Neural Networks | Lecture4 |
| Radial Basis Function Networks | Lecture5 |
| Deep Learning (CNN- RNN-GAN) | Lecture6 |
Grading Policy
Homeworks……20% | |
Project .….. 20% | |
Final Exam…..55% | |
Class Participation.....5% |
Assignments
Homework1 Due date:Homework2 Due date:
Homework3 Due date: