G53MLE
Machine
Learning
Web
Page
(2011/2012)
This module is part of the Intelligent Systems theme in the School of Computer Science. Machine learning aims to build computer systems that learn from experience or data. Instead of being programmed by humans to follow the rules of human experts, learning systems develop their own rules from trial-and-error experience to solve problems. These systems require learning algorithms that specify how they should respond as a result of experience or examples they have been shown. Machine learning is an exciting interdisciplinary field with roots in computer science, pattern recognition, mathematics and even neuroscience. The field is experiencing rapid development and has found numerous exciting real-world applications. This course gives an introduction to the principles, techniques and applications of machine learning. Topics covered include Lectures:
Tuesday 12:00 - 13:00 JC-AMEN-B18
|| Friday
15:00 - 16:00 JC-DEARING-C41
Labs: Thursday 15:00 - 16:00 JC-COMPSCI-C11
Labs 2011/12
Coursework 2011/12 Main References
I
have
divided
my
slides
into
distinctive
topics,
sometimes
these
will
be
presented
within one lecture, on other occasions they will be spread
across multiple lectures. Slides and supporting materials will appear
at least one week in advance of presentation during the course. Slides
and handouts are no replacement of textbooks, you are expected to study
recommended reading materials and do the exercise questions.
Topic 1 – Introduction and review of basic maths Readings
-
Chapter
1
of
Mitchell,
The
Discipline
of
Machine
Learning
Exercises (see end of above slides) Topic 2 –Artificial neural networks
Short Notes 02 Slides, perceptron (.ppt .pdf), ADLINE & MSE (.ppt .pdf), MLP (.ppt .pdf) Short Notes 03 Exercises (see end of above slide sets) Topic 3 – Bayesian learning
Topic 4 – Instance based learning Readings
-
Chapter
8
of
Mitchell
Exercises - (see end of above slides) Topic 5 – Clustering analysis
Readings
-
A survey paper on data clustering algorithms,
please
read
the
relevant
sections
covered
in
class.
Exercises - (see end of above slides) Topic 6 – Data processing and representations Readings
-
A tutorial on PCA by Jonathon
Schlens,
also see relevant chapters of the above references
Exercises: see examples in the slides Topic 7 – Support vector machines & other machine learning paradigms Slides
(.ppt .pdf)
Readings, Chapter 6 of reference 4 of the above reading list and tutorial articles Exercises: examples in the slide and lab 3 Topic 8 – Decision tree learning
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