This document outlines the course for ACSC468 Machine Learning offered in spring 2008/09. It provides details on the lecturer, Harris Papadopoulos, describes the course as an introduction to machine learning concepts and applications, and lists the learning objectives which include defining key terms, analyzing techniques, and applying methods to problems. The course consists of 6 lectures covering topics like concept learning, decision trees, and neural networks, and students will be evaluated based on exams, assignments, and practical participation.

1. Course Outline for ACSC468: Machine Learning
Spring Semester 2008/09
Lecturer: Harris Papadopoulos
Office: Room 205, New Building
E-mail: H.Papadopoulos@fit.ac.cy
Material: Lecture notes and supporting material (coursework assignments,
exercises, program samples, test solutions) are available on the
course home page: http://staff.fit.ac.cy/com.ph/ml/
Description
This course is intended as an introduction to the basics of machine learning. Its main
goal is to provide students with an understanding of the methodologies, technologies,
mathematics and algorithms currently used in the area. By the end of the course
students should be able to apply a variety of machine learning methods to a given
target problem.
Learning Objectives
By the end of this subject students should be able to:
• Define and explain the fundamental concepts and terminology of machine
learning and of its main areas, including concept learning, decision tree
learning, artificial neural networks, support vector machines, Bayesian
learning, instance-based learning and genetic algorithms.
• Analyse and discuss a range of machine learning techniques and their
theoretical background.
• Examine, explain and propose ways of dealing with the issues involved in the
use of machine learning methods.
• Evaluate the strengths and limitations of learning procedures and select an
appropriate learning algorithm for a given problem.
• Be able to apply machine learning methods to particular target problems and
evaluate and report the results appropriately.
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2. Lectures1
Lecture 1: Introduction to Machine Learning
Lecture 2: Concept Learning
Lecture 3: Decision Tree Learning
Lecture 4: Artificial Neural Networks
Lecture 5: Evaluation of Learning Algorithms
Lecture 6: Instance-Based Learning
Evaluation2
The final examination constitutes 60% of a student’s grade and the remaining 40% is
the student’s coursework grade, which will be calculated based on the following:
Evaluation Type After completion of Weight
Assignment Lecture 2 20%
Test Lecture 3 50%
Assignment Lecture 4 20%
Practical Participation 10%
Textbook
Tom M. Mitchell, Machine Learning, McGraw Hill, 1997.
References
1. Nello Christianini, John Shawe-Taylor, An Introduction to Support Vector
Machines: and other Kernel-based Learning Methods, Cambridge University
Press, 2000.
2. Vladimir Vovk, Alex Gammerman, Glenn Shafer, Algorithmic Learning in a
Random World, Springer, 2005.
1
A lecture does not necessarily correspond to a three period session. Some lectures will be completed
in more than three sessions, some in less.
2
The coursework evaluation may have minor changes depending on the subject’s progress.
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