The objective of the course is for the student to develop their knowledge and skills in Advanced Data Mining. After passing the course, the student should be able to:

[1].  Use the advanced data mining concepts & techniques  

[2].  Explain how those concepts and techniques work

[3].  Explain how concepts and techniques are, or should be, used in organizations

[4].  Evaluate results of applying the concepts and techniques discussed above

[5].  Analyze and reflect on the relationship between the techniques, the dataset, the problem or opportunity in
       hand, and the tools and technology used

The course is an advanced course in data mining. The course provides knowledge to address various data science problems and datasets. Focus lies on advanced machine learning techniques for classification, regression, clustering, and anomaly detection, for example decision trees, random forests, neural networks, including Support Vector Machines and Deep Learning, Expectation Maximization (EM), Markov models, and Bayesian networks.

Lectures, labs, assignments, case studies and project work. Laboratory work requires access to very high computational capacity. During the course, the students work in small groups. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions. 

Through individual tests and group/project assignment, different student abilities are examined. Those are: the ability to explain and use advanced data mining techniques and the ability to solve business problems using data mining individually and in groups.

Technical Requirements: access to PC with Windows XP, microphone, Web cam and permission to install software. Internet connection (minimum 0,5 Mbps).