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 IV Biktasheva
Computer Science
Ivb@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2017-18 Level 6 FHEQ First Semester 15

Aims

  1. To introduce students to some of the established work in the field of neural computation.
  2. To highlight some contemporary issues within the domain of neural computation with regard to biologically-motivated computing particularly in relation to multidisciplinary research.
  3. To equip students with a broad overview of the field of evol utionary computation, placing it in a historical and scientific context.
  4. To emphasise the need to keep up-to-date in developing areas of science and technology and provide some skills necessary to achieve this.
  5. To enable students to make reasoned decisions about the engineering of evolutionary ("selectionist") systems.


Learning Outcomes

Account for biological and historical developments neural computation

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

Assess the appropriate applications and limitations of ANNs

Apply their knowledge to some emerging research issues in the field

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

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

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


Syllabus


1 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)

2 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)

3 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)

4 THE INTERPRETATION PROBLEM

  • nature and issues related to problems using ANNs including symbol grounding, bootstrap, representation. Issues in practice (3 Lectures).

5 BIOLOGICALLY-INSPIRED DESIGNS AND COMPUTATIONAL NEUROSCIENCES

  • resumé based on Shepherd, Koch et al (3 Lectures)

6 INTRODUCTION TO EVOLUTIONARY COMPUTATION

  • historical review, describing the selectionist paradigm (3 Lectures)

7 BIOLOGICAL MOTIVATION

  • basic genetics, population dynamics and "fitness" (3 Lectures)

8 THE BASIC STRUCTURE OF THE GENETIC ALGORITHM (3 Lectures)

9 CASE STUDIES OF APPLICATIONS OF GENETIC ALGORITHMS (3 Lectures)

10 WHY DO GENETIC ALGORITHMS WORK ?

  • The Schema Theorem ("Building Block Hypothesis") (2 Lectures)

11 OTHER EVOLUTIONARY METHODS

  • Genetic Programming, Classifier Systems, Evolutionary Strategies (1 Lecture)




Teaching and Learning Strategies

Lecture -

Seminar -


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
Written Exam  150  Semester 1  80  Yes  Standard UoL penalty applies  Final Exam Notes (applying to all assessments) Two Class Tests, 10% of final mark each This work is not marked anonymously. Written examination, 80% of final mark. Resit exam only for PGT students.  
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Coursework  50 minutes   1st semester  10  No reassessment opportunity  Standard UoL penalty applies  Class test 1 There is no reassessment opportunity, Resit exam only for PGT students. 
Coursework  50 minutes  1st semester  10  No reassessment opportunity  Standard UoL penalty applies  Class test 2 There is no reassessment opportunity, Resit exam only for PGT students. 

Recommended Texts

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