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 |
Dr S Luo Computer Science Shan.Luo@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2021-22 | 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: |
Learning Outcomes |
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(LO1) A systematic understanding of bio-inspired algorithms that can be used for autonomous agent design and complex optimisation problems. |
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(LO2) In depth insight in the mathematics of biologically inspired machine learning and optimisation methods. |
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(LO3) A comprehensive understanding of the benefits and drawbacks of the various methods. |
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(LO4) Demonstrate knowledge of using the methods in real-world applications (e.g. logistic problems). |
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(LO5) Practical assignments will lead to hands on experience using tools as well as coding of own algorithms. |
Syllabus |
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This module will cover the following topics: Introduction to parallel problem solving from nature/overview (2 lectures) Reinforcement Learning/multi-agent reinforcement learning/replicator dynamics (8 lectures) Swarm Intelligence: Ant System, Ant Colony Optimization/Bee System/Swarm Robotics (6 lectures) Deep Learning: Restricted Boltzman Machines/auto-encoder networks/deep belief networks (8 lectures) Artificial immune systems (4 lectures) DNA computing (2 lectures) Lecture slides and reading material will be made available to the students. |
Teaching and Learning Strategies |
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Teaching Method 1 - lectures Teaching Method 2 - tutorials Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions. (b) Fully online delivery and assessment (c) Standard on-campus delivery |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
30 |
10 |
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 |
(532) Written Examination There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :2 | 180 minutes. | 70 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(532.1) Report 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :2 | max 5 pages | 15 | ||||
(532.2) Report 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :2 | max 5 pages | 15 |
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. |