COMP116 - Analytic Techniques for Computer Science


PLEASE NOTE THIS PAGE IS STILL IN DEVELOPMENT & ITS CONTENTS SUBJECT TO CHANGE


Module Information and Resources


Published Module Specification (2017-18)

(Description, Aims, Learning Outcomes, Syllabus, & Assessment)


Lecture Schedule

(accurate as advertised on Orbit (8/1/2018)

(subject to change)

MONDAY - 14.00 (2.00 pm) - in South Campus Teaching Hub - Moot Room (Room 102); (SCTH-MR, Building 120 - The location formerly known as The Law Building)
TUESDAY - 09.00 (9.00 am) in Rendall Building Lecture Theatre 7; (REN-LT7, Building 432)
THURSDAY - 11.00 (11.00 am) in Rendall Building Lecture Theatre 7; (REN-LT7, Building 432)


Tutorial Schedule


(VERY Provisional)

[NB You will be allocated (at some point) to one of the Tutorial sessions, currently at the times and locations given below (information culled from Orbit and accurate as of 8/1/2018).]

A total of seven slots have been set aside (although it is unclear at this point which will actually be used). These are:


WEDNESDAY 09.00 - Life Sciences - Seminar Room 118 (LIFS-SR1, Building 215) (co-ordinator: James Butterworth)
WEDNESDAY 10.00 - Brodie Tower - Lecture Room 107 (BROD-107, Building 233) (co-ordinator: Paul E. Dunne)
WEDNESDAY 11.00 - George Holt - Seminar Room H2.23 (GHOLT-H223, Building 231) (co-ordinator: James Butterworth)
WEDNESDAY 12.00 - Brodie Tower - Room 702 (BROD-702, Building 233) (co-ordinator: Paul E. Dunne)
THURSDAY 10.00 - Electrical Engineering - Room 201 (ELEC-201/E1, Building 235) (co-ordinator: James Butterworth)
THURSDAY 14.00 - Math. Sciences, Teaching Room 105 (MATH-105, Building 206) (co-ordinator: Paul E. Dunne)
FRIDAY 09.00 - Muspratt Building, Seminer Room 7 (JSM-SR7, Building 232) (co-ordinator: James Butterworth)


The tutorial classes will begin in WEEK 3 of the semester. That is from MONDAY 12th FEBRUARY 2018

ASSESSMENT

There will be two class tests. Each will take the form of a ten (10) question MCQ Paper and contribute 10% to the overall module mark.

The final assessment will be a two (2) hour MCQ Exam consisting of forty (40) questions and contribute 80% of the overall module mark.

The Class Tests are (provisionally) scheduled to take place in Week 4 and Week 10 of the teaching period. The specific dates intended are:

Python resources include NumPy. Information about using and obtaining this is provided here.

Tutorials on programming in Python using matrix features are also available.


Algorithmic Composition (relating to Part 4)

There is a huge collection of resources relating to Chaos, Fractal, Iterative techniques for algorithmic composition and other aspects of Computer Creativity as mentioned as an active application of Complex Number theory in AI a summary of these has been compiled here


Technical and Research Articles mentioned in module

If you are feeling adventurous and wish to look at some of the papers mentioned during the lectures most of these are available on line (in several cases as a result of University on-line journal access agreements: these require you to log-in via your University Student Account in order to view such items).

[Warning The number of *s following each reference is an (extremely) subjective "difficulty" rating and not a comment on technical quality (all are of high technical quality as is evidenced by the publication venues involved): the more *s the less accessible you may find the content to be. You should not, however, feel intimidated or remotely worried should you find even (*) articles to be incomprehensible.]


The ordering follows that used in the lecture notes: (1) from Part 4; (2) & (3) from Part 5; (4)-(8) in Part 6; (9) is the basis of ideas discussed in Part 7.


  1. P. Flajolet and A. Odlyzko. The average height of binary trees and other simple trees. Journal of Computer and System Sciences, 25(2):171-213, 1982 (******)
  2. P.E. Dunne, A. Gibbons and M. Zito. Complexity-theoretic models of phase-transitions in search problems. Theoretical Computer Science, 294(2):243-263, 2000. (**)
  3. P.E. Dunne and P.H. Leng. The Average Case Performance of an Algorithm for Demand-driven Evaluation of Boolean Formulae. Journal of Universal Computer Science, 5(5):288-306, 1999 (***)
  4. K. Bryan and T. Leise. The $25,000,000,000 Eigenvector: The Linear Algebra behind Google. SIAM Review, 48(3):569–581, 2006 (*/**)
  5. J.P. Keener. The Perron–Frobenius theorem and the ranking of football teams. SIAM Review, 35(1):80-93, 1993 (**/***)
  6. T.J.M. Bench-Capon and P. E. Dunne. Argumentation in Artificial Intelligence. Artificial Intelligence, 171, July-October 2007, 619-641 (*)
  7. J. Butterworth and P. E. Dunne. Spectral Techniques in Argumentation Framework Analysis. Proc. 6th COMMA, Potsdam, Germany, FAIA 287, 14-16 September, 2016, pages 167-178 (*)
  8. P. E. Dunne. I heard you the first time: debate in cacophonous surroundings. Proc. 6th COMMA, Potsdam, Germany, FAIA 287, 14-16 September, 2016, pages 287-298 (*/**)
  9. C.E. Shannon. A Mathematical Theory of Communication. Bell System Tech. Jnl., 27:379-423,623-656, 1948 (*/***)
    [Note The discursive and motivating commentary in Shannon's paper are presented very lucidly and in a highly readable style. The more advanced technical development of Information Theory within the article may be found a little bit more challenging. It is well worth focusing on the non-technical parts and skimming the more formal aspects of this watershed research paper.]