General provisions

The course is part of the minor field Database Technology.

Entry requirements

60 credits information systems or equivalent including 7.5 credits in databases

Learning outcomes

In terms of knowledge and understanding, after completed course the student should be able to:

• explain terms within the areas data mining and machine learning
• explain how machine learning can support answering a research question in a data mining project
• describe supervised and unsupervised machine learning methods and how they are applied, and how they can support decision-making in organizations
• explain under which conditions a particular machine learning method can be used to answer a given question
• explain the relationship of data mining and machine learning with associated fields of analytics, data science, and decision science

In terms of skills and abilities, after completed course the student should be able to:

• plan a data mining research process based the use of the machine learning methods, including problem identification, question formulation, selection of data, pre-processing method, machine learning method(s), and method for evaluation of results
• apply machine learning methods to perform analyses

In terms of evaluation and analysis, after completed course the student should be able to:

• interpret and analyze the results of a data mining process, as well as assess the effects of choices made during the process
• reflect upon the social consequences of data mining and machine learning for society, taking into account ethical aspects

Content

The course introduces the student to machine learning as a class of methods for answering a research question within the overall framework of data mining. In this course, data mining is understood as a research-oriented approach that includes problem identification, question formulation, identification and preprocessing of data, choice and application of analysis method, and analysis and evaluation of results. During the course, data will be discussed extensively, including data types and characteristics, data transformation, as well as how semi- or unstructured data, which characterize "big data", can be processed. Further, the data mining process and machine learning tasks including regression, classification, clustering, association analysis, and anomaly detection will be presented and applied. Data mining applications, and strengths and weaknesses of different methods, will also be discussed. Finally, students will be exposed to social and ethical perspectives on data mining and machine learning.

Instruction

Lectures, laborations, seminars.

Assessment

Exam, assignments, laborations, seminars.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator or a decision by the department's working group for study matters.