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Undergraduate course

Introduction to Machine Learning

Semester of Instruction

Autumn

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.

Learning Outcomes

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

Compulsory exercises

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

None

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Subject Overlap

INF280: 5 ECTS

Assessment Semester

Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Contact

Contact Information

studieveileder@ii.uib.no

Exam information

  • Type of assessment: Written examination

    Date
    18.12.2017
    Duration
    3 hours
    Withdrawal deadline
    04.12.2017