CS643: Machine Learning
| Level: | MSc |
| Credit Rating: | 15 credits (7.5 ECTS) |
| Degrees: | ACS/CompSciEng (and others, if qualified) |
| Pre-requisites: | None |
| Pre-course work: | 40 hours: 80% introductory lab, 20% preparatory reading |
| Taught week: | 40 hours: 50% lectures, 50% supervised lab |
| Post-course work: | 40 hours: 90% unsupervised lab, 10% supervised lab/clinic |
| Assessment: | 33% exam, 67% coursework (laboratory reports) |
| Lecturer: | Dr. Magnus Rattray and Dr. Aphrodite Galata |
| Limit on numbers: | 50 participants |
Introduction
Machine learning is concerned with how to automate learning from experience. This is typically accomplished by forming models which to some extent describe or summarise experiences, our data, in a useful way. For example, speech recognition software requires examples of continuous speech and will often form a different model for each different user. In this course a variety of machine learning paradigms and algorithms will be introduced which are appropriate for learning from examples with discrete or continuous-valued attributes. The course has a fairly mathematical content although it is intended to be self-contained.
Aims
This course unit aims to introduce the main algorithms used in machine learning, to introduce the theoretical foundations of machine learning and to provide practical experience of applying machine learning techniques.
Learning Outcomes
A student completing this course unit should:
- 1)
- have knowledge and understanding of the principle algorithms used in machine learning, as outlined in the syllabus below (A)
- 2)
- have sufficient knowledge of information theory and probability theory to provide a theoretical framework for machine learning (A)
- 3)
- be able to apply machine learning algorithms, evaluate their performance and appreciate the practical issues involved in the study of real datasets (C)
- 4)
- be able to provide a clear and concise description of testing and benchmarking experiments (D)
Assessment of learning outcomes
Learning outcomes (1) and (2) are assessed by
examination,
learning outcomes (1),(3) and (4) are assessed by laboratory
reports
Contribution to programme learning
A1, A2, C1, D3, D4
Reading list and supporting material
There is a CS643 web page with further details for
the current session. The main course textbook is
Alpaydin, E., ``Introduction to Machine Learning'' MIT Press,
2004. This is the new course textbook and covers a very broad
range of machine learning topics.
Additional reading
Mitchell, T. M., ``Machine Learning'' McGraw-Hill, 1997.
Introduction to machine learning, covering a broad range of
topics and algorithms. This was the previous course textbook
and provides an accessible introduction to many of the key
concepts.
Hastie, T., Tibshirani, R., Friedman, J., ``The Elements of
Statistical Learning'' Springer, 2001. An advanced textbook
taking a statistical perspective.
Bishop, C. M., ``Neural Networks for Pattern Recognition''
Clarendon Press, 1995. Good introduction to neural networks
and related statistical methods. Takes a statistical
perspective with emphasis on Bayesian inference.
Ballard, D. H., ``An introduction to Natural Computation''
MIT Press, 1997. Provides a different perspective, with
emphasis on the computational aspects of learning algorithms
in relation to computational models the brain. Covers some
material on control and hidden Markov models not discussed in
Mitchell's book.
Baldi, P., Brunak, S., ``Bioinformatics: The Machine Learning
Approach'' MIT Press, 1998. Covers a number of machine
learning applications in biology and provides a good
introduction to hidden Markov models, neural networks,
learning algorithms and Bayesian inference.
Special resources needed to complete the course unit
The matlab programming environment is used in the laboratory. A number of freely available matlab toolboxes are used.