Objectives and Content
The course covers the basics of Machine Learning, with a view towards bioinformatics applications. Topics covered are learning problems, concept learning, decision tree learning, Bayesian learning, and Support Vector Machines.
At the end of the course a student should be able to
- paraphrase the basic ideas of machine learning
- compare modeling aspects of various machine learning approaches
- evaluate machine learning approaches in terms of inductive bias
- create working implementations of machine learning algorithms
Required Previous Knowledge
At least 60 ECTS in computer science, preferably including some mathematics.
Recommended Previous Knowledge
Students need to be able to implement basic algorithms in a programming language of their choice.
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences
Compulsory Assignments and Attendance
Forms of Assessment
Oral exam. If more than 20 students take the course, a written examwill be arranged.
Compulsory exercises count towards the final grade.
Examination Support Material
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
INF280: 5 ECTS
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
mailto:firstname.lastname@example.org Student adviser
T: 55 58 42 00
For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam. The exam location will be published 14 days prior to the exam.
Type of assessment: Written examination
- 19.02.2018, 09:00
- 3 hours
- Withdrawal deadline
- Solheimsgt. 18 (Administrasjonsbygget), Eksamenslokale 3. etg.