Research Project, 6 credits

Forskningsprojekt, 6 hp

732A76

Main field of study

Statistics

Course level

Second cycle

Course type

Programme course

Examiner

Mohammad Seidpisheh

Course coordinator

Mohammad Seidpisheh

Director of studies or equivalent

Jolanta Pielaszkiewicz
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Timetable module Language Campus ECV
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 3 (Autumn 2023) 202335-202402 4 English Linköping, Valla E
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 3 (Autumn 2023) 202335-202402 4 English Linköping, Valla E

Main field of study

Statistics

Course level

Second cycle

Advancement level

A1F

Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

  • Bachelor's degree equivalent to a Swedish Kandidatexamen of 180 ECTS credits in one of the following subjects:
    • statistics
    • mathematics
    • applied mathematics
    • computer science
    • engineering
  • Passed courses in
    • calculus
    • linear algebra
    • statistics
    • programming
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish
  • At least 30 ECTS credits passed from semester 1 and 2 Master's Programme in Statistics and Machine Learning, including the course Machine Learning 9 ECTS credits, or equivalent

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.

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.

If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

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

ECTS, EC

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.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.

Department

Institutionen för datavetenskap
Code Name Scope Grading scale
PROJ Project 6 credits EC
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