Course Information Package
|Course Unit Title||INVESTMENTS AND RISK MANAGEMENT|
|Course Unit Code||MEM516|
|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:|
|Mode of Delivery||Face-to-face|
|Recommended optional program components||NONE|
|Course Contents|| - Introduction 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:|
|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|
|Language of instruction||English|