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 | Biocomputation | ||
Code | COMP305 | ||
Coordinator |
Dr U Hustadt Computer Science U.Hustadt@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2020-21 | Level 6 FHEQ | First Semester | 15 |
Aims |
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To introduce students to some of the established work in the field of neural computation. To highlight some contemporary issues within the domain of neural computation with regard to biologically-motivated computing particularly in relation to multidisciplinary research. To equip students with a broad overview of the field of evolutionary computation, placing it in a historical and scientific context. To emphasise the need to keep up-to-date in developing areas of science and technology and provide some skills necessary to achieve this. To enable students to make reasoned decisions about the engineering of evolutionary ('selectionist') systems. |
Learning Outcomes |
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(LO1) Account for biological and historical developments neural computation |
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(LO2) Describe the nature and operation of MLP and SOM networks and when they are used |
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(LO3) Assess the appropriate applications and limitations of ANNs |
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(LO4) Apply their knowledge to some emerging research issues in the field |
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(LO5) Understand how selectionist systems work in general terms and with respect to specific examples |
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(LO6) Apply the general principles of selectionist systems to the solution of a number of real world problems |
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(LO7) Understand the advantages and limitations of selectionist approaches and have a considered view on how such systems could be designed |
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(S1) Improving own learning/performance - Reflective practice |
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(S2) Improving own learning/performance - Self-awareness/self-analysis |
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(S3) Critical thinking and problem solving - Critical analysis |
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(S4) Critical thinking and problem solving - Evaluation |
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(S5) Critical thinking and problem solving - Synthesis |
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(S6) Critical thinking and problem solving - Problem identification |
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(S7) Critical thinking and problem solving - Creative thinking |
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(S8) Research skills - All Information skills |
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(S9) Research skills - Awareness of /commitment to academic integrity |
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(S10) Numeracy/computational skills - Numerical methods |
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(S11) Numeracy/computational skills - Problem solving |
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(S12) Skills in using technology - Information accessing |
Syllabus |
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BIOLOGICAL BASICS AND HISTORICAL CONTEXT OF NEURAL COMPUTATION THE MULTILAYERED PERCEPTRON KOHONEN SELF ORGANISING MAPS THE INTERPRETATION PROBLEM BIOLOGICALLY-INSPIRED DESIGNS AND COMPUTATIONAL NEUROSCIENCES INTRODUCTION TO EVOLUTIONARY COMPUTATION BIOLOGICAL MOTIVATION THE BASIC STRUCTURE OF THE GENETIC ALGORITHM (3 Lectures) CASE STUDIES OF APPLICATIONS OF GENETIC ALGORITHMS (3 Lectures) WHY DO GENETIC ALGORITHMS WORK? OTHER EVOLUTIONARY METHODS |
Teaching and Learning Strategies |
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Teaching Method 1 - Lecture Due to Covid-19, in 2020/21, 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 with minimal social distancing. |
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 |
(305) Final Exam | 70 | |||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(305.1) Class test 1 Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :1st semester | 50 minutes | 15 | ||||
(305.2) Class test 2 Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :1st semester | 50 minutes | 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. |