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
Year CATS Level Semester CATS Value
Session 2020-21 Level 6 FHEQ First Semester 15

Aims

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

(LO1) Account for biological and historical developments neural computation

(LO2) Describe the nature and operation of MLP and SOM networks and when they are used

(LO3) Assess the appropriate applications and limitations of ANNs

(LO4) Apply their knowledge to some emerging research issues in the field

(LO5) Understand how selectionist systems work in general terms and with respect to specific examples

(LO6) Apply the general principles of selectionist systems to the solution of a number of real world problems

(LO7) Understand the advantages and limitations of selectionist approaches and have a considered view on how such systems could be designed

(S1) Improving own learning/performance - Reflective practice

(S2) Improving own learning/performance - Self-awareness/self-analysis

(S3) Critical thinking and problem solving - Critical analysis

(S4) Critical thinking and problem solving - Evaluation

(S5) Critical thinking and problem solving - Synthesis

(S6) Critical thinking and problem solving - Problem identification

(S7) Critical thinking and problem solving - Creative thinking

(S8) Research skills - All Information skills

(S9) Research skills - Awareness of /commitment to academic integrity

(S10) Numeracy/computational skills - Numerical methods

(S11) Numeracy/computational skills - Problem solving

(S12) Skills in using technology - Information accessing


Syllabus

 

BIOLOGICAL BASICS AND HISTORICAL CONTEXT OF NEURAL COMPUTATION
- neurones, synapses, action potential, circuits, brain, neural computation and computational neuroscience
- associationism, instructivism, Hebb's rule, the McCulloch-Pitts Neuron, the rise of cybernetics and GST, Macey Conferences, Perceptron and non linear sepearbility, dynamical systems, emergent computation, etc (3 Lectures)

THE MULTILAYERED PERCEPTRON
- contrast with Perceptron. Representation. Feedforward and feedback phases. Sigmoidal functions, activation, generalised delta rule, adaptation and learning, convergence, gradient descent, recent developments (3 Lectures)

KOHONEN SELF ORGANISING MAPS
- nature of unsupervised learning, clustering and comparisons with statistical methods such as k-means and PCA, Iris data set,  competitive learning, the learning algorithm (3 Lectures)

THE INTERPRETATION PROBLEM
- nature and issues related to problems using ANNs i ncluding symbol grounding, bootstrap, representation. Issues in practice (3 Lectures)

BIOLOGICALLY-INSPIRED DESIGNS AND COMPUTATIONAL NEUROSCIENCES
- resumé based on Shepherd, Koch et al (3 Lectures)

INTRODUCTION TO EVOLUTIONARY COMPUTATION
- historical review, describing the selectionist paradigm (3 Lectures)

BIOLOGICAL MOTIVATION
- basic genetics, population dynamics and "fitness" (3 Lectures)

THE BASIC STRUCTURE OF THE GENETIC ALGORITHM (3 Lectures)

CASE STUDIES OF APPLICATIONS OF GENETIC ALGORITHMS (3 Lectures)

WHY DO GENETIC ALGORITHMS WORK?
- The Schema Theorem ("Building Block Hypothesis") (2 Lectures)

OTHER EVOLUTIONARY METHODS
- Genetic Programming, Classifier Systems, Evolutionary Strategies (1 Lecture)


Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Teaching Method 2 - Seminar
Description:

Due to Covid-19, in 2020/21, one or more of the following delivery methods will be implemented based on the current local conditions.
(a) Hybrid delivery, with social distancing on Campus
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teaching Method 2 - Tutorial
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

(b) Fully online delivery and assessment
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teaching Method 2 - Tutorial
Description: On-line synchronous/asynchronous sessions

(c) Standard on-campus delivery with minimal social distancing.
As our planning has already gone too far, even if the campus opens up, we will offer hybrid teaching
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teac hing Method 2 - Tutorial
Description: Mix of on-campus/on-line synchronous/asynchronous sessions


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

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

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.