1. A suite of multiple choice tests - 50% total, learning outcomes 1 - 3.
2. An end examination - 2 hours - 50% total, learning outcomes 1 - 3.

Further differentiation: Chain, Product and Quotient Rules and Partial Differentiation applied to polynomials and trig functions.
Probability - Bayes' Theorem. The Normal Distribution.
Introduction to Predicate Calculus - Quanitifiers and basic deduction.
2 and 3 Dimensional vectors - Arithmetic, magnitude and scalar product.
Complex numbers - basic arithmetic in rectangular and polar representations - Argand diagrams.
Algorithmics: Time and Storage complexity - Big O notion - Complexity classes.
Algorithm Paradigms: Divide and Conquer, Greedy Algorithms, Dynamic Programming.

24 hours of lectures (two per week) and 12 hours of tutorials (one per week) to reinforce and extend the topics introduced in the lectures.
(1:n)2 (1:20)1

Students must possess a suitable calculator with trigonometric and logarithmic functions as well as basic statistical functions.

THIS MODULE IS NO LONGER AVAILABLE TO STUDY

Discrete Mathematics for Computer Scientists, J. Truss, 1999 - Addison Wesley; ISBN: 0201360616
Understanding Algorithms and Data Structures, David Brunskill, John Turner, 1996 - McGraw-Hill Education, Europe ; ISBN: 0077091418