Mini-course on Artificial Neural Networks and Bayesian Networks

1. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Mini-course on Artificial Neural Networks and Bayesian Networks Michal Rosen-Zvi Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

2. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Section 1: Introduction Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

3. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Networks (1) • Networks serve as a visual way for displaying relationships: • Social networks are examples of ‘flat’ networks where the only information is relation between entities • Example - collaboration networks [matlab] Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

4. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Collaboration Network Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

5. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Networks (2) Artificial Neural Networks represent rules – deterministic relations - between input and output Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

6. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Networks (3) Bayesian Networks represent probabilistic relations - conditional independencies and dependencies between variables Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

7. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Outline • Introduction/Motivation • Artificial Neural Networks • The Perceptron, multilayered FF NN and recurrent NN • On-line (supervised) learning • Unsupervised learning and PCA • Classification • Capacity of networks • Bayesian networks (BN) • Bayes rules and the BN semantics • Classification using Generative models • Applications: Vision, Text Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

8. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Motivation • The research of ANNs is inspired by neurons in the brain and (partially) driven by the need for models of the reasoning in the brain. • Scientists are challenged to use machines more effectively for tasks traditionally solved by humans (example - driving a car, inferring scientific referees to papers and many others) Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

9. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Questions • How can a network learn? • What will be the learning rate? • What are the limitations on the network capacity? • How networks can be used to classify results with no labels (unsupervised learning)? • What are the relations and differences between learning in ANN and learning in BN? • How can network models explain high-level reasoning? Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004

10. תשס״דבר־ אילןאוניברסיטתהמוחלחקרברשתות המרכזהרבתחומימרוכזקורס Hebbian Learning rule Perceptron Hopfield Network Statistical Physics McCulloch and Pitts Model Pearl’s Book Minsky and Papert’s book History of (modern) ANNs and BNs Mini-course on ANN and BN, The Multidisciplinary Brain Research center, Bar-Ilan University, May 2004