Module 20236 (2013)

Module Description - Machine Learning (Extended)

The Module Description is a strict subset of the Syllabus Page, which gives more information

Module TitleMachine Learning (Extended)
SchoolComputer Science
Module Code06-20236
DescriptorCOMP/06-20236/LM
Member of StaffAta Kaban
LevelM
Credits10
Semester1
Pre-requisites 06-23069 (Introduction to AI) or equivalent
06-21254 (Mathematical Techniques for Computer Science)
Co-requisites
RestrictionsMay not be taken by anyone who has taken or is taking 06-26428 (Machine Learning) or 02640.
Contact hours
Delivery
Description The module will provide a solid foundation to machine learning. It will give an overview of many of the core concepts, methods, and algorithms in machine learning, covering several forms of supervised and unsupervised learning. It also introduces the basics of computational learning theory, leading up to more advanced topics like boosting and ensemble methods. The module will give the student a good understanding of how, why and when do various modern machine learning methods work. It will also give them experience of applying machine learning methods in practice, and an awareness of the issues, techniques and open problems posed by high dimensionality in machine learning.
Outcomes
On successful completion of this module, the student should be able to:Assessed by:
Demonstrate a knowledge and understanding of the main approaches to machine learning Examination
Demonstrate the ability to apply the main approaches to unseen examples Examination, Continuous Assessment
Demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning Examination, Continuous Assessment
Demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations Examination, Continuous Assessment
Demonstrate a practical understanding of the use of machine learning algorithmsContinuous Assessment
survey and discuss the research literature in one subfield of machine learningContinuous Assessment
AssessmentSessional: 1.5 hr examination (60%), continuous assessment (40%).
Supplementary (where allowed): By examination only.
TextsT 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
S Rogers & M Girolami , A First Course in Machine Learning, 2012
Russell S & Norvig P, Artificial Intelligence, a Modern Approach, 1995