Course Details
Course Information Package
Course Unit Title | DATA MINING | ||||||||||
Course Unit Code | MEM517 | ||||||||||
Course Unit Details | MSc Engineering Management (Electives Courses) - | ||||||||||
Number of ECTS credits allocated | 7 | ||||||||||
Learning Outcomes of the course unit | By the end of the course, the students should be able to:
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Mode of Delivery | Face-to-face | ||||||||||
Prerequisites | NONE | Co-requisites | NONE | ||||||||
Recommended optional program components | NONE | ||||||||||
Course Contents | Part A – Algorithms Multi-objective Optimization - Principles of multi objective optimization: Pareto optimal solutions, objectives in multiobjective optimization, non-conflicting objectives - Difference with single objective optimization - Dominance and pareto optimality - Classical methods: Goal programming, Min-max programming - Local Search Heuristics and Meta-heuristics - Branch and bound Techniques. Beam Search - Neighborhood structures and local search. Local optima entrapment - Single point meta-heuristics: GRASP, Simulated Annealing, Tabu Search, Variable Neighbourhood Search Evolutionary Algorithms in Optimisation - General structure of a genetic algorithm - Implementation of genetic algorithms - Dominant EA models (NSGA-II, MOEA/D) Advanced Mathematical Programming - MILP models and modelling approaches - Transformations of non-linear to MILP systems - Stochastic Programming Part B – Problem applications A series of application areas known to pose hard optimization problems will be investigated and described algorithms will be used to provide solutions. Case studies from local industries will also be used. The applications investigated will largely depend on the specializations and interest of the cohort; examples of systems examined are provided below: - Plant Scheduling systems. - Resource allocation systems - Vehicle Routing and Transportation problems - Optimization of telecommunications networks - Optimization of water reservoir systems | ||||||||||
Recommended and/or required reading: | |||||||||||
Textbooks |
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References |
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Planned learning activities and teaching methods | The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of concepts and methods. Lectures also include in-class exercises to enhance the material learning process and to assess the student level of understanding and provide feedback accordingly. Practical demonstrations and labwork are conducted in computer laboratories using various software specialised optimization tools (e.g. AIMMS modelling package, GANET multi-objective genetic algorithm package). The course material (notes, exercises, forum, etc) is maintained on the university’s e-learning platform | ||||||||||
Assessment methods and criteria |
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Language of instruction | English | ||||||||||
Work placement(s) | NO |