Computer Engineering MA, Data Mining, 6 credits

Aim

The student should develop a basic understanding of current machine learning techniques for mining large quantities of data. The student should develop skills in finding interesting patterns and building prediction models by explorative data analysis using data analysis tools based on R or Python, and preparing input, interpreting output and critically evaluating results. The student should show an ability to apply the skills in a small project in an real-world business or engineering application area such as big data visualization, business intelligence analysis, decision support systems, text/web/sensor/geo data mining, context aware applications, intelligent agents or cognitive radio.

Course objectives

The student should be able to:
• Discuss what real-world applications of data mining that are realistic and ethical
• Mine data using a tool such as the R script language, the Python Orange or SciKit Learn libraries or own implementations of algorithms
• Prepare input, interpret output and evaluate results
• Identify influential variables in a multivariate data set
• Discover patterns by association rule mining and evalute their reliability
• Develop and validate prediction models
• Follow a standard methological process reliable problem analysis, modelling and evaluation
• Apply data mining techniques on a small real-world problem

Content

• Application areas of data mining
• Data and knowledge representation (relations, attributes, sparse data, tables, decision trees, rules)
• Bayesian statistics
• Associative and sequential patterns
• Basic algorithms
• Data clustering
• Data categorization
• Data cleaning
• Data visualization
• Association rules
• Data prediction
• Laboratory exercises based on R and/or Python
• Project

Entry requirements

120 credits completed courses including the following:

Computer Engineering BA (AB) including Databases, modeling and implementation, 6 credits and a course on computer programmering, 6 credits.
Mathematics BA (A), 30 credits, including Mathematical Statistics, 6 credits.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course may be offered as a campus course or as a web-based distance course. The student time commitment is estimated to about 160 hours.

Examination form

I101: Choice of project
Grade scale: Fail (U) or Pass (G)

L101: Laboratory exercise, 0.5 Credits
Grade scale: Fail (U) or Pass (G)

P101: Project presentation, 2 Credits
Grade scale: Fail (U) or Pass (G)

T101: Exam, 3.5 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

The final grade is based on combined exam and project assessment.

Grading criteria for the subject can be found at www.miun.se/gradingcriteria.

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

If examination on campus cannot be conducted according to decision by the vice-chancellor, or whom he delegated the right to, the following applies: [Written Exam T101], will be replaced with two parts, online examination and follow-up. Within three weeks of the online examination, a selection of students will be contacted and asked questions regarding the examination. The follow-up consists of questions concerning the execution of the on-line exam and the answers that the student have submitted.

Grading system

Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.