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\section*{CS2411 - Subsymbolic Processing and Neural Networks}

\begin{description}
\item[Lecturer:] Jon Shapiro, room 2.34, extension 6253, e-mail jls.
\item[Textbooks: ] Access to \emph{one} of these two books will be
  very useful.\hfill
  \begin{enumerate}
  \item \underline{Computer Systems That Learn}, Weiss and Kulikowski,
    1991. This book is covers many topics from the course, but is at a
    elementary level.  It covers validation and testing very
    well. 
  \item \underline{Intelligent Data Analysis}, Berthold and Hand,
    1999. This book covers more of the material from the course and at
    a more detailed level. It also contains other material not covered
    in the course. Whereas Weiss and Kulikowski is in places too
    elementary for the course, this book is too technical on some
    topics. It is a more useful book to own if you continue to work in
    this area. It seems to be hard to find at the moment (out of
    stock) but is listed at \verb[http://www.whsmithonline.com[ for
    \pounds20.62. (You can search for the best price via
%   \verb[http://www.book.co.uk[.)
% Comment out above and uncomment below for pdflatex
    \href{http://www.book.co.uk}{http://www.book.co.uk}.)
  \end{enumerate}
\end{description}

\subsection*{Course Description}
\begin{description}
\item[Lectures:] 24 lectures. Wednesdays at 11:00; Fridays at 11:00 in
  LF15.
\item[Proposed Make-up lecture slot:] Wednesday at 2:00 in LF15. There are a
  few lectures which I cannot attend so need a make-up slot. 
\item[Examples classes:] There are five one-hour examples classes for
this course. These occur on week B Tuesday at 3 pm and week B Thursdays
at 10 am in room LF17.
\item[Labs:] There are five two-hour lab sessions. These occur on week
  A Tuesdays from 3--5 and Thursdays from 9--11 in the Engineering
  lab.  There are three lab exercises for this course.  Exercise~1 is
  on Bayesian classification; this lasts for two lab sessions.
  Exercise~2 is on multi-layer perceptron neural networks; it lasts
  for two lab sessions. Exercise~3 is on genetic algorithms; it lasts
  for a single session. The labs use the Matlab software under Linux
  -- a tutorial for this package will be handed out during lectures.

Labs involve experimentation with techniques developed in the course,
and don't require much programming. A lab notebook is required to
record the results of the experiments. 

 \end{description}

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%  Monday & Tuesday & Wednesday & Thursday & Friday \\ \hline
% 17 & 18 & 19 & 20 & 21 \\
%    &    &    &    & Lecture: \\ 
%    &    &    &    & Introduction          \\ \hline
% 24 & 25 & 26 & 27 & 28 \\
%    &    & Lecture:   & Example Class 1:   &  Lecture:  \\ 
%    &    & Classification   & Probability Review   & Bayesian
%  Classification   \\ \hline
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% 1  & 2  & 3  & 4  & 5  \\
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%    & Bayesian Classification   & Generalization   &
%  Classification and Learning   &  Discussion of   \\ 
%    &   &  &  & the Probability Approach \\ \hline
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 \subsection*{Schedule of topics}

\begin{tabular}{|l||c|c|c|}\hline
Week Starting: & Lecture: & Examples Class: & Lab: \\ \hline \hline
Th Sept 20 (A) & Introduction & & \\
  & Simple Classification & & \\ \hline
Th Sept 27 (B) &  Probability &  & \\ 
  & Bayesian Classification & Probability Review & \\ \hline
Th Oct 4 (A) & Testing and Validation & & \\ 
  & Learning and Generalizing & & Bayes Lab 1\\ \hline
Th Oct 11 (B) & Discussion of Probabilistic Approach & \\ 
  & Perceptrons & Bayes and Testing & \\ \hline
Th Oct 18 (A) & Gradient Descent Learning & &  \\ 
  & Minksy-Papert's Critique & & Bayes Lab 2\S \\ \hline
Th Oct 25 (B) & Multilayer Perceptrongs & & \\ 
  & MLPs II & Neural Networks&  \\ \hline
\multicolumn{4}{|c|}{Mid-term Break}\\ \hline
Mon Nov 4 (A) & MLPs III  & & \\
  &Learning Understandable Rules & & Neural Networks Lab 1\\ \hline
Mon Nov 12 (B) & Decision Trees and ID3 &  & \\
  & ID3 II & Decision Trees & \\ \hline
Mon Nov 19 (A) & Search Techniques & & \\
  & Genetic Algorithms & & Neural Networks lab 2\S \\ \hline
Mon Nov 26 (B) & Genetic Algorithms II & & \\
   & Genetic Algorithms III & Genetic Algorithms & \\ \hline
Mon Dec 3 (A) & Learning Without Teachers & & \\
   & Reinforcement Learning & & Genetic Algorithms Lab\S  \\ \hline
Mon Dec 10 (B) & Review I & & \\
   & Review II & & Lab Catchup \\ \hline 
\end{tabular}
Labs in which work is due are denoted with \S.


\subsection*{Reading by Syllabus Topic}
Listed below are the relevant sections of the recommended reading and
the supplemental readings for various topics of the course. It is
expected that you will need to read at least one piece of supportive
material for each topic. 
\begin{description}
\item[Introduction(Lectures 1):] Weiss and Kulikowski (WK) Chapter 1;
  Berthold and Hand (BH) Chapter 1. 
\item[Bayesian Decision and Classification(Lectures 2,3,4,7):] Probability
theory handout, WK Chapter 3; BH Chapter 2, sections 2.1, 2.2, 2.4.3. 
\item[Learning theory I: Validation(Lecture 5):] WK Chapter 2, BH
  Chapter 2, sections 2.5 and 2.6. 
\item[Learning theory II: Generalisation(Lecture 6): ] Not in WK. BH
  sections 7.4.4, 7.4.5, 7.4.6 are relevant.
\item[Single-Layer Neural Networks (Lectures 8--10): ] Perceptrons
handout; WK Chapter 4; BH Chapter 7 through 7.2
\item[Multi-Layer Neural Networks (Lectures 11--13): ]
  Backpropagation handout; WK Chapter 4; BH Chapter 7, sections 7.3 --
  7.4, 7.8.
\item[Learning from Non-numeric Data(Lectures 14--16): ] Information
theory handout, WK Chapter 5; Chapter 6, particularly section 6.4.
\item[Non-symbolic Search Techniques(Lectures 17, 18): ] Handouts. BH
  Chapter 9. 
\item[Genetic Algorithms(Lectures 19, 20):] Genetic Algorithms
handout; BH Chapter 9, sections 9.3, 9.5, 9.6, 9.7
\item[Learning Without Teachers(Lectures 21, 22):] Handouts.
\end{description}
%\pagebreak

%\pagebreak

\subsection*{Other Sources} Here are some other sources of information
about the topics in the course. This includes both simple and advanced
treatments of the material.

\begin{itemize}

\item \underline{Adaptive Pattern Recognition and Neural Networks}, Yoh-Han Pao
(1989). \\ This is a very good book. It was a textbook for the course,
but is now out of print. It is good on neural networks; less good
on Bayesian approaches. It contains more mathematical
detail and advanced material than Weiss and Kulikowski. A very good
book to get from the library and follow throughout the course. 

\item \underline{Neural Computing}, Beale and Jackson, 1990
 \begin{quote}
An elementary book on neural networks. Good treatment of Bayesian
classification, neural networks, and some of the search aspects of 
the course at a simple level. Contains nothing on validation or  
non-numerical methods.
\end{quote}

\item \underline{Machine learning, Neural and Statistical Classification},
D. Michie, D.J. Spiegelhalter and C. C. Taylor, 1994
\begin{quote}
Not a textbook. A collection of articles describing current 
applications of and approaches to the techniques studied
in the course.
\end{quote}

% \item \underline{Techniques in Computational Learning}
% C. J. Thornton, 1992. 
% \begin{quote}
% A book on methods of learning from data, including supervised learning
% and unsupervised learning in neural networks, and learning in decision
% trees, as well as learning methods not covered in this course. It
% contains nothing
% on generalisation, validation, or Bayesian methods, unfortunately. 
% \end{quote}

\item \underline{Modern Heuristic Techniques for Combinatorial
    Problems}, C. Reeves (editor), 1993. 
\begin{quote}
An overview of search techniques including simulated annealing and
genetic algorithms. 
\end{quote}

\item \underline{Genetic Algorithms in Search, Optimization, and Machine Learning},
D. Goldberg, 1989.
 \begin{quote}
The standard reference on Genetic Algorithms.
\end{quote}

% \item \underline{Neural Networks, A comprehensive
%     Foundation}, Simon Haykin, 1994. Second edition, 1998.
%  \begin{quote}
% An excellent book on neural networks. A problem with it is that
% it contains almost no mention of
% applications. Advanced and comprehensive, although overly formal in
% places.  Second edition is very up to
% date. 
% \end{quote}

\item \underline{Neural Networks for Pattern Recognition},
Christopher Bishop, 1996
\begin{quote}
This book contains
an excellent treatment of Bayesian approaches, neural networks, and
generalisation, as well as other aspects not treated here. Somewhat
more advanced in its treatment, its study will give a deep
understanding of the material. Nothing on unsupervised learning,
decision trees, or search in discrete spaces.  
\end{quote}

\item \underline{Pattern Classification and Scene Analysis}, Duda and Hart, 1973 \begin{quote}
The classic work on traditional statistical methods and Bayesian methods.
\end{quote}


\end{itemize}

\end{document}
Here is the alternative reading by syllabus topic.

\begin{description}
\item[Bayesian Classification:] Beale and Jackson, Chapter 2; Michie,
 Spiegelhalter
 and Taylor (MST)
  Chapters 2,3,4; Bishop Chapters 1 and 2, Pao, Chapter 2. 
\item[Generalisation and Validation:] MST Chapter 7; Bishop Chapters
    1.5, 9.1, 9.2, and 9.8.
\item[Supervised Learning Neural Networks:] Beale and Jackson Chapters
  3 and 4; MST Chapter 6; Thornton Chapters 9 -- 14; Bishop Chapters 3
  and 4; Pao, Chapters 5,6.
\item[Unsupervised Learning in Neural Networks:] Beale and Jackson
  Chapter 5; Thornton Chapter 18.
\item[Learning from Non-Numeric Data:] MST Chapter 5; Thornton, Chapter 5 and
  6; Pao, Chapter 4.
\item[Subsymbolic Search Techniques and Genetic Algorithms:] Reeves
  Chapters 1, 2, and 4;
  Goldberg Chapter 2.
\end{description}
\end{document}





