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Haykin S. Neural Networks And Learning Machines... |LINK|

Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists.

Haykin S. Neural Networks and Learning Machines...


Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:

Contents/Inhalte Our objective is to examine the foundations, representations and applications ofhybrid systems in order to support various themes in intelligent information systems,cognitive robotics and interactive systems. While traditional approaches have focusedon symbolic representations alone, newer hybrid symbolic/neural/statisticalapproaches are often nature-inspired drawing inspirations from biological systems,neural systems or cognitive performance. We want to explore these foundations inthe context of building more sophisticated adaptive interaction systems, learningagents, self organising information systems and bio-inspired robotic systems. Forbuilding such nature-inspired computing systems we examine the embedding ofneural, statistical and/or symbolic representations into knowledge-based adaptiveinformation agents. Applications can include intelligent information systems,interactive systems, adaptive engineering, data/text mining systems, cognitive andneuroscience-inspired robots, speech/language systems, intelligent web agents andhybrid techniques for medical diagnosis. We will teach the concepts linked to andwith examples from our own research projects from the UK and EU.

  • Literature/Literatur Indicative: Marsland, S. Machine Learning: An Algorithmic Perspective. 2009.

  • Wermter S., Sun R. Hybrid Neural Systems. Springer Verlag, Heidelberg,2000.

  • Wermter S., Riloff E., G. Scheler (Ed). Connectionist, Statistical andSymbolic Approaches to Learning for Natural Language Processing. Springer Verlag,Berlin, 1996.

  • Haykin S. Neural networks and learning machines. Prentice Hall, 2008

- In the written exam the student must demonstrate his/her knowledge of the course material and to report their experience, with critical awareness, in the applications and evaluation of Machine Learning models. Students have the opportunity to develop a project realizing a learning system simulator (typically a simple neural network) and to validate it through benchmarks.The written test is typically based on material with the result of the project, including the code and a written report of the experimental results. The material is delivered in advance by the student.- The oral test consists in an interview (with the possibility of including written questions) between the candidate and the course lecturers on all parts of the program and, where appropriate, in the discussion on the written test. During the oral exam the student must be able to demonstrate his/her knowledge of the course material and be able to discuss the reading matter thoughtfully and with propriety of expression, also showing the ability to relate the various notions acquired and a sufficient awareness of the limits and potential of learning systems. To take the oral exam, students must have obtained a sufficient grade in the written test. 041b061a72


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