Postgraduate Course: Machine Learning and Sensorimotor Control (INFR11014)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 11 (Postgraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/mlsc |
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| Course description |
Control of complex, compliant, multi degree of freedom sensorimotor systems like humanoid robots or autonomous vehicles have been pushing the limits of traditional control theoretic methods. This course aims at introducing adaptive and learning control as a viable alternative. The course will take the students through various aspects involved in motor planning, control, estimation, prediction and learning with an emphasis on the computational perspective. We will learn about statistical machine learning tools and methodologies particularly geared towards problems of real-time, online learning for sensorimotor control. Issues and possible approaches for multimodal sensor integration, sensorimotor transformations and learning in high dimensions will be discussed. This will be put in context through exposure to topics in human motor control, experimental paradigms and the use of computational methods in understanding biological sensorimotor mechanisms. |
Course Delivery Information
Summary of Intended Learning Outcomes
1 - Describe the components of 'traditional' model based control and critically assess it's limitations in the real-time control of compliant, high dimensional sensorimotor systems.
2 - Design and evaluate experimental paradigms to test various biological control hypotheses and identify potential ways of reducing control complexity.
3 - Apply the machine learning tools and algorithms learnt in the module to design an efficient adaptive (learning) control scheme for a given real world control problem.
4 - Implement a basic ?model-based= control schema with learning in MATLAB/C using dimensionality reduction techniques.
5 - Carry out benchmark comparisons against the state of the art learning methods and optimization/planning strategies. |
Assessment Information
Written Examination 50
Assessed Assignments 35
Oral Presentations 15
Assessment
There will be two coursework assignments accounting for 35% of the course marks. One of these might involve a group mini-project involving implementation of learning control on an artificial simulated plant or practical implementation of sensor integration strategies. 15% of the marks will be based on a paper presentation from a list of human motor control papers.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
| Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
| Not entered |
Contacts
| Course organiser |
Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary |
Miss Gillian Watt
Tel: (0131 6)50 5194
Email: gwatt@inf.ed.ac.uk |
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copyright 2010 The University of Edinburgh -
1 September 2010 6:10 am
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