ICS 586: Neural Networks Dr. Lahouari Ghouti Information & Computer Science Department.

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Presentation transcript:

ICS 586: Neural Networks Dr. Lahouari Ghouti Information & Computer Science Department

ICS 586: Neural Networks First Semester 2008/2009 (081) Instructor: Instructor: Dr. Lahouari Ghouti Information and Computer Science Department Office: Building 22 – Room 128 Tel: 1922

Grading Policy TaskWeight Four Quizzes10% Homeworks10% Research Paper (Lecture) Presentation10% One Major Exam15% Final Exam25% Research Project [Proposal 5% - Final Report + Prototype 25% - Class Presentation 10%] 40%

Tentative Schedule Introductory Meeting [Introduction to Neural Networks] Single Layer Perceptron Multilayer Perceptron ADALINE The LMS Algorithm Backpropagation Learning Overfitting, Cross-Validation, and Early Stopping Simple Recurrent Networks Pattern Classification (Guest Speaker?) Radial Basis Functions Support Vector Machines Competitive Learning and Kohonen Nets Hebbian Learning Principal Components Analysis (PCA) Adaptive Principal Components Extraction (A Student) Non-Negative Matrix factorization (A Student) Hopfield Networks and Boltzmann Machines Bayesian Networks (A Student) Hidden Markov Models (A Student) Extreme Learning Machines (A Student)

Programming Environment We will be using MATLAB in this course. If you know, that’s fine If you do not, you will need to learn it.

Getting Started With ANNs Foundations of Neural Computation: Understand the operation of single neurons or small neural circuits. Detailed biophysical models of nerve cells (receptors, ion channels, membrane voltage), and collections of cells.

Varieties of “Neural Networks” Research 1- Neuronal Modeling 2- Computational Neuroscience 3- Connectionist / Parallel Distributed Processing (PDP) Models 4- Artificial Neural Networks (ANNs)

Connectionist (PDP) Modeling Model human cognition in a brain-like way: Massively parallel constraint satisfaction. Distributed activity patterns instead of symbols. Models are fairly abstract.

ANN Landscape

Artificial Neural Networks Models: Simple, abstract,.neuron-like. computing elements; local computation. Applications: Pattern recognition, adaptive control, time series prediction. This is where the money gets made! Reference: Pomerleau 1993: ALVINN

Artificial Neural Networks: The Beginnings W. S. McCulloch and W. Pitts (1943) Logical calculus of the ideas immanent in nervous activity. Philosophy of Science 10(1), Warren McCullochWalter Pitts Revolutionary Idea: think of neural tissue as circuitry performing mathematical computations!

The McCulloch-Pitts Neuron Linear weighted sum of inputs: Learning rule: Nonlinear, possibly stochastic transfer function: Transfer function g(x)

What to do now? Check WebCT for course syllabus + soft copy of textbook Start learning MATLAB if you do not know it! Select a topic you want to present in the class from the tentative schedule (first-come first-serve basis!) Select your time slot to discuss with me your project. The earlier you start, the better off you will be.