FIT4012 Advanced topics in computational science - Semester 2, 2013

All sciences are increasingly relying on computational support and the growth of many branches of science has only become possible due to the availability of efficient computational methods. The common basis of such methods are; numerical methods and high performance computing. Topics for this unit include: Numerical Methods, High Performance and Parallel Computing, Optimisation and Operations Research Bioinformatics, Simulation, Visualisation and Modelling.

Mode of Delivery

Contact Hours

2 hrs lectures/wk

Workload requirements

Weekly workload commitments are:

• 2 hour lecture
• a minimum of 5 hours personal study and lecture preparation
• a minimum of 5 hours for working on programming and written assessments

Unit Relationships

Prerequisites

Completion of the Bachelor of Computer Science or equivalent to the entry requirements for the Honours program. Students must also have enrolment approval from the Honours Coordinator.

Chief Examiner

Campus Lecturer

Academic Overview

Learning Outcomes

• understand the place of computational methods in the chosen field of specialisation and their relation to non-computational approaches;
• compare and contrast alternative computational approaches in this domain;
• critically evaluate the limits and capabilities of these methods;
• be able to select, design and test computer programs in the domain;
• where appropriate, be able to use the standard computational packages in the chosen domain effectively for practical problem solving.

Unit Schedule

*Unit Schedule details will be maintained and communicated to you via your learning system.

Assessment Summary

Assignment and Examination, relative weight depending on topic composition. When no exam is given students will be expected to demonstrate their knowledge by solving practical problems and maybe required to give an oral report.

Teaching Approach

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

Assessment Tasks

Participation

• Assessment task 1

  Title:

  Programming Exercises

  Description:

  Short programming exercises on evolutionary simulation and synthesis.

  Weighting:

  30%

  Criteria for assessment:

  • Correctness
  • Accuracy
  • Efficiency
  • Quality of documentation
  • Quality of results
  • Evidence of testing
  • Statistical analysis
  • Coding use
  • Inventiveness of solutions

  Due date:

  Week 3, Friday, 5pm

• Assessment task 2

  Title:

  Written Essay

  Description:

  Write a short academic paper on a topic in evolutionary simulation and synthesis. The lecturer will provide a list of possible topics.

  Weighting:

  20%

  Criteria for assessment:

  Marks will be awarded based on the criteria listed below. The questions listed indicate the kind of questions that will be asked when your work is assessed.

  • Logical structure: is the paper well structured (e.g. title, abstract, introduction, body, conclusion, references)? Does it present its material in a logical and clear way?
  • Writing quality: does every word count? Has the author avoided ‘padding out’ the text with waffle in order to get to the necessary word count? Are the main points of the paper clear and convincing, with solid arguments and proper referencing to the literature?
  • Language, spelling and grammar: has the paper been proof-read? Are there spelling mistakes? Do sentences make sense? Are there any grammatical errors? Is it easy to establish what the writer is trying to say?
  • Quality of analysis: how well has the topic being researched? How clearly does it establish the important points and arguments. Are the references appropriate and adequate?
  • Original contribution: what does the paper contribute to the topic beyond just listing opinions or work done by others? How original is the paper?

  Due date:

  Week 6, Friday, 5pm

  Remarks:

  Please note that it is important to correctly attribute material that is not your own. Your paper will contain a bibliography, listing the work of others that you have consulted. The number of references you consult is up to you, as a rough guide most papers of this size will have somewhere between 6 - 20 references. Do not ‘bulk up’ your bibliography with unnecessary references or ones that you have not actually read.
  
  Consider the authority and origin of your research sources. Favour books, journals and conference proceedings that are peer reviewed and from reputable publishers over web pages, for example.
  
  At least 80% of your references should originate from sources other than the Internet (electronic versions of journal or conference papers can count towards this quota).

• Assessment task 3

  Title:

  Problem Solving and Programming Exercise

  Description:

  Problem solving and programming exercise on combinatorial problems, computational complexity and search paradigms. The assessment questions will be available from Week 7.

  Weighting:

  30%

  Criteria for assessment:

  • Correctness and accuracy of the solution
  • Efficiency of the algorithm
  • Complexity analysis
  • Use of appropriate programming practices

  Due date:

  Week 9, Friday, 5pm

• Assessment task 4

  Title:

  Research Proposal

  Description:

  Formulate a research proposal on one of the topics covered during the last 6 weeks of the unit (Weeks 7 - 12).

  Weighting:

  20%

  Criteria for assessment:

  • Critical awareness of relevant literature 
  • Strength of argument 
  • Use of information and literature to sustain argument 
  • Awareness of strengths and weaknesses of approach 
  • Appropriate and accurate use of language 

  Due date:

  Week 12, Friday, 5pm

Learning resources

Reading list

Sean Luke (2009): "Essentials of Metaheuristics", Lulu, Available for free download at: http://www.cs.gmu.edu/~sean/book/metaheuristics/

A.E. Eiben and J.E. Smith (2007): "Introduction to Evolutionary Computing", (2nd Edition) Springer, Natural Computing Series

Stochastic Local Search, Foundations and Applications, by Holger H. Hoos and Thomas Stützle, http://www.sls-book.net/

How to Solve It: Modern Heuristics, by Zbigniew Michalewicz and David B. Fogel, http://www.amazon.com/How-Solve-It-Modern-Heuristics/dp/3540224947

Feedback to you

• Informal feedback on progress in labs/tutes
• Graded assignments with comments

Extensions and penalties

Returning assignments

Assignment submission

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.

Recommended Resources

Access to a C, C++ or Java compiler and IDE environment. These are available in University computer labs.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

Your feedback to Us

Previous Student Evaluations of this Unit

Student feedback has shown the unit is structured well. To make sure materials are current lecture notes have been updated and new visual references have been added.