Research Project, 6 credits

Main field of study

Course level

Advancement level

Course offered for

• Masters Programme in Statistics and Machine Learning

Entry requirements

A bachelor’s degree in one of the following subjects: statistics, mathematics, applied mathematics, computer science, engineering, or equivalent. Completed courses in mathematical analysis, linear algebra, statistics and programming are required. 
Courses in Data Mining or Machine Learning covering at least 15 ECTS are also required.

Documented knowledge of English equivalent to Engelska B/Engelska 6.

Intended learning outcomes

After completion of the course, the student should at an advanced level be able to
- apply in the field of data mining or machine learning in a real setting
- plan, perform and report on an individual task 
- discuss research and development work in machine learning or related areas

Course content

The content of the course is adapted to the problem addressed. The student joins an ongoing project or research in data mining or machine learning and studies the origin of the problem and the research related to the it, and analyzes the given problem by using methods and tools from data mining or machine learning. 

Teaching and working methods

The work is performed individually with support and guidance of a supervisor. Language of instruction: English. 

Examination

The course is examined by written project reports, and a final oral examination.
Detailed information about the examination can be found in the course’s study guide.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Department