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SSST Subjects

DISCRETE MATHEMATICS

Computer Science

|

Information Systems

 
 

Programme(s) where module is offered

  • BSc Computer Science with Electrical Engineering
  • BSc Computer Science with Economics
  • BSc Computer Science with Management
  • BSc Computer Science with International Relations
  • BSc Computer Science with Political Science
  • BSc Information Systems with Electrical Engineering
  • BSc Information Systems with Economics
  • BSc Information Systems with Management
  • BSc Information Systems with International Relations
  • BSc Information Systems with Political Science
 

Status (core, option, free choice)

Core

 

FHEQ Level

4

 

Unit Value

8 ECTS

 

Semester taught

Spring

 

Pre-Requisite Modules or Qualifications

None

 

Module Code

Math180

 

Module coordinator

Dr. Mirna Udovicic

 

Applicable From

2016

 

Educational Aims of the Module

  • This is an undergraduate module that covers the fundamentals of discrete mathematics.
  • The primary aim is to familiarise students with mathematical concepts that are central to those disciplines, including logic, sets, proofs, graphs, and general mathematical maturity
 

Module Outline/Syllabus

  • Statements, conjunction, disjunction, negation, implication, equivalence
  • Quantifiers, validity of arguments  Sets, subsets, set operations
  • Weak induction, strong induction, well-ordering principle
  • Divisibility, remainders, quotient-remainder theorem
  • Primes, prime factorisation, unique prime factorisation, GCD, LCM
  • Relations, equivalence relations, partial order relations, classes of equivalence
  • Functions, injective, surjective, bijective
  • Basic counting, combinations, permutations, multisets
  • Basic probability related to counting techniques
  • Axioms of probability, properties
  • Conditional probability
  • Bayes theorem
  • Binomial theorem and applications
  • Graphs, connectedness, connected components
  • Eulerian cycles, Hamiltonian cycles
  • O,o, theta classes of functions, applications to running time of algorithm
  • Introduction to game theor
 

Student Engagement Hours

Type Number per Term Duration Total Time
Lectures 30 2 hours 60 hours
Tutorials 15 2 hours 30 hours
Total Guided/Independent Learning Hours 110
Total Contact Hours 90
Total Engagement Hours 200
 

Assessment Method Summary

Type Number Required Duration / Length Weighting Timing / Submission Deadline
Final exam 1 180 minutes 50% End of semester
Mid-term exam 1 90 minutes 20% Mid-semester
Quiz 4 120 minutes 15% Weeks 4, 6, 12, 14
Problem solving 3 700 words 15% Weeks 5, 10, 14
 

Module Outcomes

Teaching and Learning Strategy:

  • Lectures 
  • Tutorials and discussions emphasising a large number of problems related tomaterial covered in lectures 
  • Activities in the laboratory related to the core material (mostly group and individual projects especially in the area of algorithms and number theory) 
  • Laboratory sessions with tutor-lead support 
  • Use of quizzes to test student subject knowledge 

Assessment Strategy

  • Final Exam 
  • Mid-term exam 
  • Quiz
  • Problem solving 
 

Key Texts and/or other learning materials

Set text

  • Johnsonbaugh, R.,(2005), Essential Discrete Mathematics, MacMillan Publishing

Supplementary Materials

  • Rosen, K., (2012), Discrete Mathematics and its Applications, Global Edition, McGraw-Hill
  • Scheinerman, E., (2006), Mathematics: A Discrete Introduction, Cengage
  • Discrete Mathematics (Journal), http://www.elsevier.com/mathematics
  • SIAM Journal on Discrete Mathematics (SIDMA), http://www.siam.org/journals/sidma.php/
  • Epp, S., (2011), Discrete Mathematics with Applications, 4th Edition, Brooks & Cole
  • MIT, (2014), Mathematics for Computer Scientists lecture series, [online], http://ocw.mit.edu/modules/electricalengineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/video-lectures/ (Accessed 24th November 2015).

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.  More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook.  The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : Autumn 2016

Date approved by School Learning and Teaching Committee: 28th September 2016

Date approved by School Board of Study : 12th October 2016

Date approved by University Learning and Teaching Committee: 2nd November 2016

Date of Annual Review: December 2017

       
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