MS2S560 - Computational Mathematics 11 Dec 2020 - 31 Aug 2027 | Version 2
Associated Module Information
| Module Code: | MS2S560 | ||
|---|---|---|---|
| Module Title: | Computational Mathematics | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Samuel Jobbins | ||
| Module Team: | Stephanie Perkins, Joel Harris | ||
| First Intended Intake: | SEP 2016 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 5 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 101029 - computational mathematics | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 2 |
|---|---|
| Valid From | 11 Dec 2020 |
| Valid To | 31 Aug 2027 |
Module Aims
To provide a knowledge of basic mathematical and statistical concepts used to analyse the efficiency and correctness of algorithms.
To enable students to represent and manipulate knowledge employed in problem solving approaches, and to appreciate the power and limitations of symbol systems.
Content Summary
Problem Solving: defining a problem, finding a solution, recurrence, elegance vs efficiency, Divide and Conquer, simple ideas of algorithms, representations of problems as graphs and networks for hand-application of algorithms, route inspection problem
Logic: classical propositional logic, implication, equivalence, truth tables, DeMorgan's Laws, negation; well formed formulas. The limitations of propositional logic for knowledge representation, and classical First-Order Predicate Logic (symbolic formal system); extension to well-formed formulas and satisfiability, predicates and arguments, quantifiers and quantified variables, inference rules and resolution, dealing with uncertainty and fuzzy Logic, probability and modelling uncertainty.
Models of Computation: algorithm, sequential computation, simple machines, regular expressions, models of parallel & distributed computation, complexity, time vs space complexity,
landau notation (big-O), hard problems, description complexity, parallel complexity, applied complexity (prime numbers, factorization).
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Tutorial | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Apply a range of mathematical and statistical techniques to represent and analyse standard computing techniques. |
| LO2 | Apply a range of mathematical and statistical techniques to represent and analyse standard problem solving methodologies. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Portfolio | Portfolio 1 | 0 | N/A | 70 | No | 40 | |
| Written Assignment (CW) | Time Constrained Assessment (CW) 1 | 90 | 1 | 30 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Portfolio 1 | ✘ | ✘ | |
| Time Constrained Assessment (CW) 1 | ✔ | ✔ | |