FFR 135 – Neural Networks (FY 4210 –Neural Networks) 7.5 ECTS credit units Grading: Fail, 3, 4, 5 Level: Advanced Department: Applied Physics Teaching language: English 1st quarter In programs Engineering Physics FCMAS (Complex Adaptive Systems) NKASM (Complex Adaptive Systems) TDATA (Datateknik) TELTA (Elektroteknik) TITEA (Informationsteknik, årskurs 3) TITEA (Informationsteknik Årskurs 4) TAUTA (Automation och mekatronik) Examiner Professor Bernhard Mehlig Aim Neural networks are distributed computational models inspired by the structure of the human brain, consisting of many simple processing elements which are connected in a network. Neural networks are increasingly used in the engineering sciences for tasks such as pattern recognition, prediction and control. The theory of neural networks is a inter-disciplinary field (neurobiology, computer science and statistical physics). The course gives an overview and a basic understanding of neural-network algorithms. can develop an understanding of when neural networks are useful in application problems Learning outcomes After successfully completing this course the students shall be able to understand and explain strengths and weaknesses of the neural-network algorithms discussed in class determine under which circumstances neural networks are useful in real applications distinguish between supervised and unsupervised learning and explain the key principles of the corresponding algorithms efficiently and reliably implement the algorithms introduced in class on a computer, interpret the results of computer simulations describe principles of more general optimisation algorithms write well-structured technical reports in English presenting and explaining analytical calculations and numerical results communicate results and conclusions in a clear and logical fashion Content Introduction to neural networks (McCulloch –Pitts neurons, associative memory problem, Hopfield model and Hebb’s rule, storage capacity, energy function) Stochastic neural networks (noise, order parameter, mean-field theory for the storage capacity) Optimisation Supervised learning: perceptrons and layered networks (feed-forward networks, multilayer perceptrons, gradient descent, backpropagation, conjugate-gradient methods, performance of multilayer networks) Unsupervised learning (Hebbian learning, Oja’s rule, competitive learning, topographic maps) Recurrent networks and time-series analysis (recurrent backpropagation, backpropagation in time Reinforcement learning Organization Lectures., set homework problems, examples classes. Web-based course evaluation. Literature Lecture notes will be made available. They are based on the course book: Hertz, A. Krogh, and R. G. Palmer, Introduction to the theory of neural computation, AddisonWesely, Redwood City (1991). Additional reading: S. Haykin, Neural Networks: a comprehensive foundation, 2nd ed., Prentice Hall, New Jersey (1999) Examination The examination is based on exercises and homework assignments (100%). The examinator must be informed within a week after the course starts if a student would like to receive ECTS grades. Prerequisites Sufficient knowledge of Mathematics (analysis in one real variable, linear algebra), basic programming skills .