Courses: INF283 Introduction to Machine Learning - Autumn 2018
ECTS Credits
10
Level of Study
Bachelor/master/PhD
Full-time/Part-time
Full-time
Language of Instruction
English
Teaching semester
Autumn
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. Machine learning is used within "big data" and data analysis. 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 algorithms on concrete data set.
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
The student should:
- have a good overview of how machine learning is used in different contexts in the society
Required Previous Knowledge
At least 60 ECTS in computer science, preferably including some mathematics.
Recommended Previous Knowledge
MAT121, STAT110
Be able to implement basic algorithms in a programming language of your own choice.
Credit Reduction due to Course Overlap
INF280: 5 ECTS
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, 4 hours per week
Exercises, 2 hours per week
Compulsory Assignments and Attendance
Compulsory exercises are valid for one subsequent semester.
Forms of Assessment
Oral exam. If more than 20 students take the course, a written exam may 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.
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 January 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.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