Statistics and Machine Learning in Bioinformatics, 7.5 credits

Contents

The course introduces fundamental methods in regression, classification and cluster analysis with a focus on applications in bioinformatics. Students receive an overview of both classical statistical methods and modern machine‑learning approaches for analysing large and complex biological datasets. Practical implementation and critical evaluation of method strengths and limitations are included.

Expected learning outcomes

Knowledge and understanding
1. Explain fundamental methods in regression, classification and cluster analysis.
2. Understand strengths and limitations of common statistical and machine‑learning methods.

Skills and abilities
3. Select and apply appropriate methods for analysing biological datasets.
4. Perform analyses in relevant software and present results orally and in writing.
5. Evaluate and justify methodological choices based on problem formulation and data characteristics.

Judgement and approach
6. Critically evaluate analyses performed using regression, classification and cluster analysis.

Required Knowledge

At least 60 credits in biology, biomedicine, molecular biology, molecular ecology, molecular evolution, genetics or related subjects. Of these, at least 7.5 credits must be in bioinformatics, 15 in molecular biology/molecular ecology/molecular evolution, 7.5 in biochemistry/chemistry/mathematics/statistics, 7.5 in genetics, and 15 credits at second‑cycle level in bioinformatics or a related field. English 6/Level 2.

Form of instruction

Teaching consists of lectures, applied exercises and computer laboratories. The language of instruction is normally English but may be Swedish if appropriate. Students must have access to a personal computer with internet access and software installation rights. Course literature is specified in Ladok.

Examination modes

The course is assessed through a written examination that evaluates theoretical understanding, as well as through written assignments that assess applied analytical skills. The overall course grade is one of the following: U (Fail), G (Pass), or VG (Pass with Distinction).

To receive a grade of G (Pass) for the course, the student must achieve a passing result (G) on all examinations. To receive a grade of VG (Pass with Distinction), all examinations must be passed and the written examination must be awarded the grade Pass with Distinction (VG).

Students who do not pass the regular examination are entitled to a re-sit examination. The first re-sit must be offered no later than two months after the regular exam, but not earlier than ten working days after the results have been announced. For exams held in May or June, the first re-sit may be offered within three months. At least one additional re-sit (make‑up exam) must be offered within one year.

Examiners may decide to deviate from the modes of assessment in the course syllabus. Individual adaption of modes of assessment must give due consideration to the student’s needs. The adaption of modes of assessment must remain within the framework of the intended learning outcomes in the course syllabus. Students who require an adapted examination must submit a request to the department holding the course no later than 10 days before the examination. The examiner decides on the adaption of the examination, after which the student will be notified.

Transitional provisions

In the event that the syllabus ceases to apply or undergoes major changes, students are guaranteed at least three examinations (including the regular examination opportunity) according to the regulations in the syllabus that the student was originally registered on for a period of a maximum of two years from the time that the previous syllabus ceased to apply or that the course ended.