Mål og innhald
The course introduces Machine Learning, with a view towards data analysis applications. Topics covered are supervised learning (classification and regression), unsupervised learning including clustering, decision tree learning, Bayesian learning, and working with textual data.
A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- has theoretical knowledge about the principles of machine learning
- has a basic understanding of the contemporary machine learning algorithms
- has a broad knowledge about the use of machine learning in data analysis, its advantages and limitations
- can analyze and design machine learning solutions for data analysis applications
Krav til forkunnskapar
INFO132 or equivalent. Basic understanding of programming and algorithms.
INF264 (10 sp)
Krav til studierett
The course is open to all students at the University of Bergen.
Arbeids- og undervisningsformer
Lectures, seminars and data labs, normally 2 + 2 hours per week for 12-15 weeks.
- Compulsory assignments, which have to be approved in the teaching semester.
- Participation: compulsory attendance at labs (at least 75%).
Approved compulsory requirements are valid for the two following semesters.
4 hour written exam.
Update spring 2021: As part of the measures to limit the risk of corona infection the form of assessment will be:
- Group assignment where students demonstrate their ability to analyze and design machine learning solutions (30% of grade)
- 2 hour digital home exam (70% of grade)
Hjelpemiddel til eksamen
All written material in paper form is allowed on the exam.
Assessment in teaching semester and in the following semester (for the students who have valid obligatory assignments and attendance).
Alle emne blir evaluert i tråd med UiBs kvalitetssystem for utdanning.
Telephone 55 58 90 00