Machine Learning
- ECTS credits10
- Teaching semesterSpring
- Course codeINFO284
- Number of semesters1
- LanguageEnglish
- Resources
Main content
Level of Study
Bachelor
Teaching semester
Spring
Objectives and Content
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.
Learning Outcomes
A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
The candidate
- 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
Skills
The candidate
- can analyze and design machine learning solutions for data analysis applications
Required Previous Knowledge
INFO132 or equivalent. Basic understanding of programming and algorithms.
Recommended Previous Knowledge
INFO132 or equivalent. Basic understanding of programming and algorithms.
Credit Reduction due to Course Overlap
INF264 (10 ECTS)
Access to the Course
The course is open to all students at the University of Bergen.
Teaching and learning methods
Lectures, seminars and data labs, normally 2 + 2 hours per week for 12-15 weeks.
Compulsory Assignments and Attendance
- 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.
Update: In the spring semester 2021, the requirement to attend 75% of the seminars will not apply due to the corona situation. However, it is highly recommended that students attend as much as possible.
Forms of Assessment
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)
Examination Support Material
All written material in paper form is allowed on the exam.
Grading Scale
The grading system has a descending scale from A to E for passes and F for fail.
Assessment Semester
Assessment in teaching semester and in the following semester (for the students who have valid compulsory assignments and attendance).
Course Evaluation
All courses are evaluated according to UiB's system for quality assurance of education.
Contact
Telephone 55 58 90 00
Exam information
Type of assessment: Group assignment and home exam
- Withdrawal deadline
- 27.04.2021
Exam part: Group assignment
- Submission deadline
- 11.05.2021, 14:00
- Examination system
- Inspera
- Digital exam
Exam part: Home examination
- Assignment handed out
- 09.06.2021, 09:00
- Submission deadline
- 09.06.2021, 11:00
- Examination system
- Inspera
- Digital exam