MA4001  Logic and Problem Solving (2022/23)
Module specification  Module approved to run in 2022/23  
Module title  Logic and Problem Solving  
Module level  Certificate (04)  
Credit rating for module  30  
School  School of Computing and Digital Media  
Total study hours  300  


Assessment components 


Running in 2022/23 (Please note that module timeslots are subject to change) 

Module summary
The module aims are to give the students an understanding of how problems can be solved systematically, plan their solutions and write them in the form of algorithms. This module also develops a range of mathematical techniques including set theory, logic, relations, functions and operational research techniques. In addition it gives a grounding in standard software packages, to give students an understanding of their use in problem solving as well as to make students able to apply these packages appropriately in subsequent modules.
Syllabus
• Puzzles: developing logical reasoning, introducing systematic approach to solving puzzles, developing appropriate strategies to solve puzzles. LO1
• Linear Programming, Sensitivity analysis, simulation modelling, Maths of Finance and Breakeven analysis
• Propositional Logic: representation of simple verbal arguments; truthtables; logical equivalence, validity and consequence, logic circuits. Predicate logic.
• Sets: introduction to notation; set operations; Venn diagrams; universal, empty set and subsets; set identities (De Morgan etc.); duality; power sets, ordered pairs and Cartesian products.
• Algorithms: understanding how problems can be solved systematically, plan their solutions and write them in form of algorithms (e.g. Euclid's Algorithm)
• Combinatorics: combinations, permutation and probability
• Relations: representations of relations (matrix and digraph); equivalence relations; partitions; partial orderings.
• Functions: ways of defining functions; composition; inverse functions. LO2,LO3,LO4
Balance of independent study and scheduled teaching activity
Learning technologies will be used for providing the teaching materials (e.g. WebLearn). The module will be taught by a mixture of lectures, supervised computer laboratory sessions and selfstudy practical exercises. In particular, the lectures will be used to introduce the various concepts and principles of the module's topics or demonstrate worked examples. Each lecture will be followed by a practical supervised session where the students will be able to apply/experiment with the various notions introduced in the lectures, using examples and following detailed instructions. The materials that will be used in the practical sessions will allow each student to work at his/her own speed. Furthermore, students will be pointed to selfstudy exercises which they will attempt in their own time. The students will also be expected to spend time on private study and on preparation for the assessments.
Learning outcomes
LO1 Create algorithmic methods of realworld problems and to develop and present the solutions.
LO2 Understand the meaning of mathematical definitions of sets/propositions and perform set/logic operations.
LO3 Understand the meaning of mathematical definition of relations and to determine which relations are equivalence relations.
LO4 Understand the meaning of mathematical definition of functions, to use it to construct functions and to determine which functions are onetoone.
Assessment strategy
This module is assessed through tests and coursework.
The first component will be takeaway coursework and it will assess LO1.
In the first test the students are assessed on sets and logic (LO2).
The first test and feedback is designed so that students can identify any deficiencies in their learning strategies and put corrective strategies in place at an early point in studying Logic. The second test will assess the learning outcomes LO3 to LO4.
Reassessment Strategy for the group coursework:
Students who have reassessment opportunity in the Group Coursework will be required to work on the first sit coursework and submit it as an individual coursework on Weblearn.
Bibliography
Core Text:
Rod Haggarty (2006), Discrete Mathematics for Computing, Addison Wesley.
Recommended Reading:
Quantitative Techniques by Terry Lucey (Author) Publisher: Cengage Learning EMEA; 6 edition 2002.