MSc in Engineering Management

Course Information Package

Course Unit TitleINVESTMENTS AND RISK MANAGEMENT
Course Unit CodeMEM516
Course Unit DetailsMSc Engineering Management (Electives Courses) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Define and explain the major principles, techniques and research issues of data mining.
  2. Analyse and discuss a range of data mining techniques and their theoretical background and recognize the situations where they could be applied successfully.
  3. Examine, explain and propose ways of dealing with the issues involved in the use of data mining techniques.
  4. Identify and formulate the application of data mining for solving engineering problems.
  5. Conduct a detailed data mining investigation of a practical problem and critically analyse and evaluate the results.
Mode of DeliveryFace-to-face
PrerequisitesNONECo-requisitesNONE
Recommended optional program componentsNONE
Course ContentsIntroduction to Data Mining: What is data mining; Who uses data mining and why; Situations where data mining is useful;  Simple examples of problems and data that will be used throughout the course to demonstrate and explain the data mining techniques; Real life application examples of data mining in engineering problems.
Data Mining Problem Types and Data: Classification, regression, association learning and clustering; Examples, attributes and attribute types; Preparing the data for mining.
Data Mining Techniques: Inferring rudimentary rules – 1R; Statistical modeling – Na�ve Bayes; Decision Trees; Choosing the best splitting attribute; Tree pruning; Decision tree pros and cons; Association Rule Mining; Evaluation of association rules; Problems and limitations of association rules; Linear models: Linear regression, Logistic regression; Artificial Neural Networks; Biological motivation; Perceptrons; Multilayer Neural Networks; Neural Network training; Nearest Neighbour approaches; Collaborative Filtering; Clustering; Why cluster the data; The K-means clustering method; Evaluating clusters.
Data Storage and Visualisation: Data Warehousing; Data Marts; Metadata; Online Analytical Processing; Where does OLAP help; OLAP examples.
Engineering the Input and Output: Attribute selection; Filter and wrapper methods; Searching the attribute space; Discretising attributes; Unsupervised discretisation; Entropy-based discretisation; Converting discrete to numeric attributes; Data transformations; Principal components analysis; Random projections; Text to attribute vectors; Time series; Combining multiple models; Bagging; Boosting; Additive regression; Option trees.

Recommended and/or required reading:
Textbooks
  • Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, 2005.
References
  • Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006.
  • Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann, 2006.
  • Michael Berry and Gordon Linoff, Data Mining Techniques, 2nd Edition, Wiley, 2004.
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 techniques. 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 the Weka data mining workbench. These will give students experience on the application of the techniques discussed in class on real data.
The course material (lecture notes, exercises, etc.) will available to students through the university’s e-learning platform.

Assessment methods and criteria
Assignments30%
Project20%
Readings10%
Final Exam40%
Language of instructionEnglish
Work placement(s)NO