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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Business Analytics with Soft Computing (CMSE11429)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis course provide students with fundamental theory and applications of heuristics, meta-heuristics and evolutionary computations; other topics to be covered in this course include Bayesian Analysis and Networks.
Course description Academic 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, meta-heuristics ,hyper-heuristics, evolutionary computations as well as Bayesian Analysis to address decision making problems in business. Variety 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: This course consists of 5 lectures.
(Lecture 1) Introduction to soft computing
(Lecture 2) Heuristics and meta-heuristics
(Lecture 3) Evolutionary computation
(Lecture 4) Hyper-heuristics
(Lecture 5) Bayesian Analysis and networks

Student Learning Experience
Students are expected to learn basic concepts and theories from 5 two-hour lectures for 5 weeks. In four two-hour tutorial sessions, they will learn how to apply the basic concepts and theories learned in the lectures to solve business problems.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
Academic year 2019/20, Not available to visiting students (SS1) Quota:  47
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 8, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 80 )
Assessment (Further Info) Written Exam 0 %, Coursework 90 %, Practical Exam 10 %
Additional Information (Assessment) Individual Report (90% weighting)
Assesses Learning Outcomes 1,2,3 and 4.

Group Presentation (10% weighting)
Assesses Learning Outcomes 1 and 4.

Students will be divided to small groups to work on a single topic. Every member of each group is expected to write a report on a given problem individually describing the problem and its solution technique.
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:
  1. Express the concept and methods of soft computing using proper terminologies
  2. Analyse decision problems in business settings using soft computing techniques
  3. Implement a soft computing technique, interpret results and formulate managerial guidelines and make recommendations
  4. Communicate findings effectively and efficiently verbally and in writing.
Reading List
Basic Business Statistics: Concepts and Applications (by David M. Levine, Timothy C. Krehbiel, Mark L. Berenson)
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, meta-heuristic 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
-analysing 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
-work as an effective member of a group
KeywordsNot entered
Course organiserDr Nader Azizi
Tel: (0131 6)51 1491
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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