Neural Networks 2011-2012

Neural Networks
Academic Year 2011–2012

Instructor: Prof. R. De Leone

  • Learning Outcomes
    • Identify the general characteristics and the computational paradigm for Artificial Neural Networks
    • Illustrate the main differences between various Artificial Neural Networks architectures’
    • Illustrate the main algorithm for supervisioned and unsupervisioned training
    • Utilize specific software for Artificial Neural Networks
  • Contents
    • Artificial Neural Networks architectures and computational paradigm
    • Characteristics of Artificial Neural Networks and classification
    • The biological neuron and the artificial neuron: weights and activation functions.
    • Artificial Neural Networks as universal approximators
    • Static, dynamic and recurrent networs
    • Perceptron and Multilayer Feedforward networks: the back-propagation algorithm
    • Learning and generalization
    • Unsupervisioned learning
    • Support Vector Machines
  • Final Exam Oral exam. A project will be assigned to each student.


  • J. Hertz, A. Krogh, R.G. Palmer Introduction to the theory of neural computation, Addison-Wesley, 1991
  • Further material would be made available during the course.


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