Prerequisites:( signals and systems 044130
and Introduction to Probability h 104034 )
Identical Courses: Neural Networks for Control/Diagnostic 036049
Introduction to Machine Learning 236756
An introductory course on learning systems in the context of:
· Signal processing
· Artificial intelligence and control
· Problems of classification
· Regression and clustering
· Neural networks: multi-level perceptrons and radial basis functions
· Decision trees
· Elements of the learning theory: the Bayesian approach, hypothesis spaces
· Dimensionality reduction using principal components
· Classification using support vector machines
· Reinforcement learning
· T.M. Mitchell, Machine Learning, McGraw-Hill, 1997.
· E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004.
· Duda, Hart and Stork, Pattern Classification, 2nd Ed., Wiley, 2001. [recommended]
· C. Bishop, Pattern Recognition and Machine Learning, Springer , 2007. [recommended]
· Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Springer, 2001.
Lecturer: Prof. Koby Crammer