Artificial Intelligence 2 Module 3: Module Description
Aims and Objectives
The module covers two related areas:
Learning from Data and
Coping
with Incomplete Knowledge. The aim is to give a first introduction
to these areas as well as introduce general techniques that are used within
the areas.
Syllabus
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Machine learning systems, learning as search, decision trees, Perceptrons,
neural networks.
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Modelling uncertainty, probabilistic modelling using Bayes Nets, inference
and learning in Bayes Nets.
Intellectual Skills Development
This module develops two complementary aspects often needed in AI: modelling
of problems or situations and developing algorithmic ideas for solving
them. Both aspects are treated in the two areas. The techniques are general
and should be useful in other contexts as well.
Activities
There will be 18 lectures and 5 tutorials devoted to the module.
Assessment
Examination questions in the final AI2Bh exam based on this module.
There will be two assignments based on this module.
Context
The material lends itself to mathematical presentation at some points.
Where appropriate relevant background will be covered in lectures.
References
(***) Stuart Russell and Peter Norvig, "Artificial
Intelligence: A Modern Approach", Second Edition, Prentice Hall, 2002.
This page is maintained by the course lecturer,
Jacques Fleuriot, Room 3.07 Appleton Tower (
jdf@inf.ed.ac.uk)
