Module Details |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Machine Learning and BioInspired Optimisation | ||
Code | COMP532 | ||
Coordinator |
Prof K Tuyls Computer Science K.Tuyls@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2016-17 | Level 7 FHEQ | Second Semester | 15 |
Aims |
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In this module we focus on learning agents that interact with an initially unknown world. Since the world is dynamic this module will put strong emphasis on learning to deal with sequential data unlike many other machine learning courses. The aims can be summarised as:
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Learning Outcomes |
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A systematic understanding of bio-inspired algorithms that can be used for autonomous agent design and complex optimisation problems. |
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In depth insight in the mathematics of biologically inspired machine learning and optimisation methods. |
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A comprehensive understanding of the benefits and drawbacks of the various methods. |
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Demonstrate knowledge of using the methods in real-world applications (e.g. logistic problems). |
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Practical assignments will lead to hands on experience using tools as well as coding of own algorithms. |
Syllabus |
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1 |
This module will cover the following topics:
Lecture slides and reading material will be made available to the students. |
Teaching and Learning Strategies |
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lectures - students will be expected to attend three hours of formal lectures in a typical week |
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tutorials - one hour of weekly seminar given by students in groups, or one hour of tutorial by instructor. |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
30 students will be expected to attend three hours of formal lectures in a typical week 10 one hour of weekly seminar given by students in groups, or one hour of tutorial by instructor. |
40 | |||||
Timetable (if known) | |||||||
Private Study | 110 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Unseen Written Exam | 180 | 2 | 75 | Yes | Standard UoL penalty applies | written examination Notes (applying to all assessments) The first report will be due in week 6 and the second report will be due in week 10. The first report will concern a task related to the state of the art literature in RL, evolutionary game theory, swarm intelligence (with a max of 5 pages). The report of the 2nd task will revolve around a student presentation during the tutorial sessions on one of the bio-inspired methods discussed during formal lectures (with a max of 5 pages). |
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Coursework | max 5 pages | 2 | 10 | Yes | Standard UoL penalty applies | report |
Coursework | max 5 pages | 2 | 15 | Yes | Standard UoL penalty applies | report |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. Explanation of Reading List: |