Module 02640 (2012)
Module Description - Machine Learning
The Module Description is a strict subset of the Syllabus Page, which gives more information
| Module Title | Machine Learning | ||||||||||||||
| School | Computer Science | ||||||||||||||
| Module Code | 06-02640 | ||||||||||||||
| Descriptor | COMP/06-02640/LI | ||||||||||||||
| Member of Staff | Ata Kaban | ||||||||||||||
| Level | I | ||||||||||||||
| Credits | 10 | ||||||||||||||
| Semester | 1 | ||||||||||||||
| Pre-requisites | 06-23069 (Introduction to AI) (or equivalent) | ||||||||||||||
| Co-requisites | None | ||||||||||||||
| Restrictions | May not be taken by anyone who has taken or is taking 06-20236 (Machine Learning (Extended)). | ||||||||||||||
| Contact hours | |||||||||||||||
| Delivery | 2 hrs lectures, exercise and laboratory classes per week. | ||||||||||||||
| Description | The module will provide a good foundation to machine learning. It will compare and contrast human learning with machine learning. It will examine the limitations of machine learning, the role of hypothesis bias and hypothesis representation. | ||||||||||||||
| Outcomes |
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| Assessment | Sessional: 1.5 hr examination (80%), continuous assessment (20%). Supplementary (where allowed): By examination only. | ||||||||||||||
| Texts | T Mitchell, Machine Learning, 1997 R Sutton & A. Barto, Reinforcement Learning: an Introduction, 1998 Nello Cristianini & John Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods, 2000 R.O. Duda, P.E. Hart & D.G.Stork, Pattern Classification, 2000 Pierre Baldi, Paolo Frasconi, and Padhraic Smyth , Modelling the Internet and the Web, 2003 Russell S & Norvig P, Artificial Intelligence, a Modern Approach, 1995 |