Courses: INF283 Introduction to Machine Learning - Autumn 2017
Credits
10.0Language of Instruction
English
Teaching semester
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
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 Information
studieveileder@ii.uib.no