Courses: INF264 Introduction to Machine Learning - Spring 2019
ECTS Credits
10
Level of Study
Master/PhD
Full-time/Part-time
Full-time
Language of Instruction
English
Teaching semester
Autumn. First time autumn 2019
Objectives and Content
Machine learning is a branch of artificial intelligence focusing on algorithms that enable computers to learn from and change behavior based on empirical data. The course gives an understanding of the theoretical basis for machine learning and a set of concrete algorithms including decision tree learning, artificial neural networks, Bayesian learning, and support vector machines. The course also includes programming and use of machine learning algorithms on real-world data sets.
Learning Outcomes
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
At the end of the course the student should:
- understand the basic ideas of machine learning
- be able to compare modeling aspects of various machine learning approaches
Skills
At the end of the course the student should:
- develop and implement machine learning algorithms
- apply and evaluate machine learning algorithms on real data sets
General competence
At the end of the course the student should:
- have a good overview of how machine learning can be used in different contexts in the society
Required Previous Knowledge
Recommended Previous Knowledge
Programming skills., INF102 or equivalent.
Good mathematical background, especially linear algebra, calculus, and probability (e.g., MAT111, MAT121, STAT110).
Credit Reduction due to Course Overlap
INF283, INFO284, 10sp.
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences
Teaching and learning methods
Lectures, max. 4 hours per week
Exercises, 2 hours per week
Independent projects
Compulsory Assignments and Attendance
Compulsory assignments are valid for one subsequent semester.
Forms of Assessment
Written exam (3 hrs). The compulsory exercises can be graded and this grade can count for the final grade. Both the exam and the compulsory exercises must be passed.
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.
Assessment Semester
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
Reading List
The reading list will be available within June 1st for the autumn semester and December 1st for the spring 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.
Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB, or contact mailto:studieveileder@ii.uib.nomailto:studieveileder@ii.uib.noStudent adviser
Course Administrator
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.
Contact Information
Student adviser:
mailto:studieveileder@ii.uib.nomailto:studieveileder@ii.uib.noStudent adviser
T: 55 58 42 00