|
Week |
Subjects |
Related Preparation |
Learning Outcome
|
| 1 |
Biological motivation. Historical remarks on artificial neural networks. Applications of artificial neural networks. A taxonomy of artificial neural network models and learning algorithms. |
Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 2 |
General artificial neuron model. Discretevalued perceptron model, threshold logic and their limitations. Discretetime (dynamical) Hopfield networks. Hebb’s rule. Connection wieght matrix as an outer product of memory patterns. |
Chapter 1. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 3 |
Supervised learning. Perceptron learning algorithm. Adaptive linear element. Supervised learning as output error minimization problem. Gradient descent algorithm for minimization. Least mean square rule. |
Chapter 2. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 4 |
Single layer, continuous valued perceptron. Nonlinear (sigmoidal) activation function. Delta rule. Batch mode and pattern mode gradient descent algorithms. Convergence conditions for deterministic and stochastic gradient descent algorithms. |
Chapter 3. Chapter 4: Sections 4.1, 4.2, 4.16. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 5 |
Multi layer perceptron as universal approximator. Function representation and approximation problems. Backpropagation Learning. Local minima problem. Overtraining. |
Chapter 4: Sections 4.4, 4.5, 4.8, 4.10, 4.12. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 6 |
Midterm Exam I. |
|
| 7 |
Batch and pattern mode training. Training set versus test set. Overfitting problem. General practices for network training and testing. Signal processing and pattern recognition applications of multilayer perceptrons. |
Chapter 4: Sections 4.3, 4.10., 4.11, 4.13, 4.14, 4.15, 4.19, 4.20. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. |
| 8 |
Radial Basis Function (RBF) network. Backpropagation learning for determining linear weights, centers and widths parameters of RBF networks. Random selection of centers. Input versus input-output clustering for center and width determination. Regularization theory, mixture of Gaussian (conditional probability density function) model and neurofuzzy connections of RBF networks. |
Chapter 5. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761 |
| 9 |
Parametric versus nonparametric methods for data representation. Unsupervised learning as a vector quantization problem. Competitive networks. Winner takes all networks. Kohonen’s self organizing feature map. Clustering. |
Chapter 9. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. |
| 10 |
Signal processing applications of artificial neural networks. Principal component analysis. Data compression and reduction. Image and 1D signal compression and transformation applications of artificial neural networks. |
Chapter 8. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761 |
| 11 |
Midterm Exam II. |
|
| 12 |
Pattern recognition applications of artificial neural networks. Artificial neural networks for feature extraction. Nonlinear feature mapping. Data fusion. Artificial neural networks as classifiers. Image and speech recognition applications. |
Sections 1.4,1.5., 3.11, 4.7, 5.8, 6.7, S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 13 |
Implementation of artificial neural networks models and associated learning algorithms for signal processing, pattern recognition and control in MATLAB numerical software environment. |
L. Fausett, Fundamentals of Neural Networks, Chapter 6, Prentice Hall, ISBN-13: 978-0133341867 |
| 14 |
Cumulative review of artificial neural networks models, learning algorithms and their applications. |
S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
| 15 |
Semester Review |
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| 16 |
Final Exam |
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