Neural Networks

Overview | Contents | Prerequisites | Course materials | Homework |
Projects | Bibliography | Links

Overview:

This is a one-semester course for the third year students of the Computer science section. The aim of the course is to introduce the main principles of neural computation.

Basic notions: computational neurons, artificial neural networks, architectures, functioning, learning.
Single-layer feedforward networks with supervised learning (the perceptron's, Widrow-Hoff's and delta algorithms). Applications in classification.
Multi-layer feedforward networks with supervised learning (the BackPropagation algorithm). Applications for pattern recognition and function approximation.
Single-layer feedforward networks with unsupervised learning (correlative and competitive algorithms). Applications for principal component analysis and clustering. Self-organizing maps.
Radial basis function networks. Applications in function approximation.
Recurrent neural networks. Applications for associative and optimization tasks, for time series prediction and image processing.
Genetic algorithms and genetic programming. Applications for optimization problems.

Prerequisites:

Programming Languages
Basic Linear Algebra
Numerical Analysis
Statistics

Course materials

Lecture 1: Introduction in Computational Intelligence
Lecture 2:( Artificial Neural Networks Design
Lecture 3: One layer Neural Networks. Classification problems
Lecture 4: Ona layer Neural Networks. Mapping problems.
Lecture 5: Multilayer Neural Networks. Variants of the Backpropagation Algorithm
Lecture 7: Radial Basis Functions Networks
Lecture 8: Unsupervised Competitive Learning (ART)
Lecture 9: Unsupervised Competitive Learning (SOM) and unsupervised Correlative Learning (PCA)
Lecture 10: Recurrent Neural Networks (I)
Lecture 11: Recurrent Neural Networks (II)
Lecture 12-13: Evolutionary Design of Neural Networks

Lab1: Introduction in Matlab Neural NetworksToolbox.
Lab2: Single Layer Perceptrons. Linearly Separable Classification problems. Application: readPattern.m, classification.m
Lab3: Multilayer Perceptrons. Nonlinear regression and prediction. Applications: regression.m, prediction.m, date.dat
Lab4: Radial Basis Function Networks. Nonlinear regression and prediction. Applications: regressionRBF.m, regressionRBFin.m, prediction.m, date.dat
Lab5: Neural networks with unsupervised competitive learning. Data clustering. Applications: kmeans.m, clusteringWTA.m
Lab6: Self-organizing Maps. Data analysis (som_Data.m). Elastic net for TSP (ElasticNet_TSP.m)
Lab7: Hopfield networks (associative memories) and Elman networks (time series modelling). Applications: HopfieldNet.ma, ElmanNet.ma

Homework 1 Implementation of a single layer perceptron for character recognition
Homework 2 Implementation of a neural network with one hidden layer for prediction
Homework 3 Implementation of a competitive neural network for data clustering

Bibliography:

Kosko, B. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, 1992.
Kung, S.Y. , Digital neural networks, Prentice Hall, 1993.
Dumitrescu, D., Costin, H. Retele neuronale; teorie si aplicatii. Teora, Bucuresti, 1996.
Dumitrecu D., Algoritmi genetici si strategii evolutive, Microinformatica Cluj, 2000.
Müller, B., Reinhardt, J. Neural Networks – An Introduction. Springer-Verlag, Berlin, 1990.
Kohonen, T. Self–Organization and Associative Memory. Series in Information Sciences, vol. 61, Springer–Verlag, Berlin, 1989.
Wasserman, P. Neural Computing – Advanced Methods. Van Nostrand Reinhold Inc., Computer Science Series, 1993.
Masters, T. Practical Neural Networks Recipes in C++. Academic Press, Boston, 1993.
Ripley, B.D., Pattern Recognition and Neural Networks, Cambridge University Press, 1996.

Carti existente la BCUT (http://www.bcut.ro/):

GELENBE, Erol (ed.), NEURAL networks, 1992.
MASTERS, Timothy, Advanced algorithms for neural networks, 1995.
HAGAN, Martin T., Neural network design, 1995.
BELTRATTI, Andrea, Neural networks for economic and financial modeling, 1996.
SKAPURA, David M., Building neural networks, 1996.
ELLACOTT, Stephen W., MATHEMATICS of neural networks, 1997.
RUAN, Da (ed.), INTELLIGENT hybrid systems, 1997
MITCHELL, Melanie, An introduction to genetic algorithms, 1996.
KRUSE, Rudolf, Fuzzy-systems in computer science, 1994.
TERANO, Toshiro (ed.), FUZZY systems theory and its applications, 1992.
ISHIKAWA, Akira, Analysis and evaluation of fuzzy systems, 1995.
MORABITO, F.C. (ed.), ADVANCES in intelligent systems, 1997.
DUBOIS, Didier, Fuzzy sets and systems, 1980.
LOOTSMA, Freerk A., Fuzzy logic for planning decision ma..., 1997.

Links:

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