Postgraduate Course: Soft Computing (CMSE11448)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provide students with fundamental theory and applications of heuristics, metaheuristics and evolutionary computations; other topics to be covered in this course include Bayesian Analysis and Networks. |
| Course description |
Real life decision problems are often too complicated to be modelled by e.g., mathematical tools. Even if they are modelled, this type of problems are often intractable and extremely challenging to solve. In recent years, the emergence of soft computing as an alternative way of solving problems in areas such as optimisation has attracted attentions from both academics and practitioners. This course offers alternative approaches to solve complex problems which could otherwise be difficult to solve by traditional techniques. It aims at training students in the field of heuristics, metaheuristics ,hyperheuristics, evolutionary computations as well as Bayesian Analysis to address decision making problems in business. Varity of applications will be examined with more emphasis on transportation, logistics and fleet management. The course further aim is to enhance students understanding of the critical nature of designing and/or selecting appropriate methods for solving complex decision problems. It provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field.
Outline Content:
Introduction to soft computing
Heuristics and metaheuristics
Evolutionary computation
Hyper and/math-heuristics
Bayesian Analysis and networks
Student Learning Experience
Students are expected to learn basic concepts and theories from lectures. In tutorial sessions, they will learn how to apply the basic concepts and theories learned in the lectures to solve business problems.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students, or by permission of course organiser and programme director. Please contact the course secretary. |
Course Delivery Information
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| Academic year 2020/21, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 12,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Course Assessment: Coursework (100%)
Report (Individual) 100% weighting - Assesses LO1,LO2,LO3, LO4
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| Feedback |
The assessments will be marked according to the University common marking scheme. Feedback on formative assessed work will be provided in line with the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically discuss and express the concept and methods of soft computing using proper terminologies.
- Analyse decision problems in business settings using soft computing techniques.
- Implement a soft computing technique, interpret results and formulate managerial guidelines and make recommendations.
- Communicate findings effectively and efficiently verbally and in writing.
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Additional Information
| Graduate Attributes and Skills |
Research & Enquiry:
On completion of the course, students should be able to:
- Understand how optimisation and other complex problems could be modelled using approximation and Bayesian techniques
- Understand the basic framework of heuristic, metaheuristic and hyper-heuristic approaches as well as Bayesian Networks and know how to apply them to practical situations
- Identify underlying assumptions of the approximation and Bayesian modelling techniques and critically evaluate their validity on applications
Personal & Intellectual Autonomy:
On completion of the course, students should be able to:
- think independently and exercise personal judgement while solving complex optimisation problems
- analyse situations and applying creative and inventive thinking to develop an appropriate solution technique to an optimisation problem
- implement the solution technique(s) and review decisions based on appropriate techniques
Communication skills
On completion of the course, students should be able to:
- explain implications of network models/analysis to general audiences
- develop appropriate documentation to communicate the result of a small project |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Nader Azizi
Tel: (0131 6)51 1491
Email: Nader.Azizi@ed.ac.uk |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: Emily.Davis@ed.ac.uk |
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