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\section*{CS648: Neural Networks}

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\label{CS648_sec}

\begin{tabular}{ll}
Level:                     & MSc  \\
Credit Rating:             & 15   \\
Degrees:                   & ACS/CS  \\  % ACS for 1st semester, ACS/CS for 2nd semester modules
Pre-requisites:            & None  \\  % None or brief statement of pre-requisite
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Pre-course work:           & 30 hours: 66\% introductory exercise, 33\% preparatory reading \\
Taught week:               & 40 hours: 60\% lectures, ...40\% supervised lab \\
Post-course work:          & 50 hours: 50\% unsupervised lab 50\% essay \\
Assessment:                & 33% exam 40% laboratory work 27% essay\% exam, ...\% lab, ...\% coursework \\
Lecturer:               & Dr. Jonathan Shapiro 
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\subsection*{Introduction}

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This course focuses on the foundations of neural network theory and
the application of neural network models in engineering, cognitive
science, and artificial intelligence. 
The course will present the major neural network
paradigms: attractor neural network models of memory, a sequence of supervised learning
models of increasing complexity, a sequence of unsupervised clustering and categorisation networks, reinforcement learning networks, and aspects of learning theory. Practical experience with the models will
be integrated into the course. A final computer project and an essay
on related work will constitute post-course work. 

\subsection*{Aims}

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\subsection*{Learning Outcomes}

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A student completing this course unit should:

\begin{enumerate}

\item[1)]
   have an understanding of the concepts and techniques of neural networks through the study of
    the most important neural network models.
   \progspec{(A)}

\item[2)]
   have a knowledge of sufficient theoretical background to be able to
   reason about the behaviour of neural networks
   \progspec{(A and B)}

\item[3)]
   be able to evaluate whether neural networks are appropriate to a
   particular application  
   \progspec{B)}

\item[4)]
   be able to apply neural networks to particular applications, and to
   know what steps to take to improve performance
   \progspec{(B)}

\item[5)]
   have knowledge of research literature on neural networks in one
   particular domain, and be able to put new work into context of that
   literature
   \progspec{(C and D)}


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\subsection*{Assessment of learning outcomes}

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Learning outcomes (1), (2), (3) and (4) are assessed by examination, \\
learning outcome (1), (2), and (4) by examination and in the laboratory and \\
learning outcomes (5) by an essay related to chosen final laboratory project

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\subsection*{Contribution to programme learning}

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% A2, B1, B2, B3, C4, D3

...

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\subsection*{Reading list and supporting material}

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Haykin, S., Neural Networks - A Comprehensive Foundation (2nd Edition). Macmillan,
1999.

\subsection*{Special resources needed to complete the module}

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Laboratory work will be done in matlab using the Neural Network
Toolbox, and public domain toolboxes.

\subsection*{Detailed syllabus}

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\paragraph{Motivation [1].} 
The role of neural networks in engineering,
artificial intelligence, and cognitive modelling.

\paragraph{Supervised learning in neural networks [4].} 
Feed-forward
neural networks of increasing complexity, gradient descent learning
and extensions,  learning and generalization theory

\paragraph{Computation and dynamical systems [4]}
Hopfield model of content-addressable memory,
    Hopfield-Tank approach to optimisation, resistive networks for
vision models, complex dynamical learning models.

\paragraph{Reinforcement Learning [4]}
The problem of reinforcement
learning, Arp learning, Q-learning, TD-learning. Generalization and
function approximation.

\paragraph{Unsupervised Learning [4]}
Competitive learning,
Self-organizing feature maps, ART networks, GWR networks.

\paragraph{Selected Applications [8]}

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