Machine Learning
Undergraduate course
- ECTS credits
- 10
- Teaching semesters Spring
- Course code
- INFO284
- Number of semesters
- 1
- Teaching language
- English
- Resources
- Schedule
- Reading list
Course description
Objectives and Content
The course introduces Machine Learning, with a view towards data analysis applications. Topics covered are supervised learning (classification and regression) with deep learning, unsupervised learning including clustering, reinforcement learning, and the practice of machine learning.
Learning Outcomes
A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
The candidate
- has theoretical knowledge about the principles of machine learning
- has a basic understanding of the contemporary machine learning algorithms
- has a broad knowledge about the use of machine learning in data analysis, its advantages and limitations
Skills
The candidate
- can analyze and design machine learning solutions for data analysis applications
Level of Study
Bachelor
Semester of Instruction
Spring
Required Previous Knowledge
INFO132 or equivalent. Basic understanding of programming and algorithms.
Recommended Previous Knowledge
INFO132 or equivalent. Basic understanding of programming and algorithms.
Credit Reduction due to Course Overlap
INF264 (10 ECTS)
Access to the Course
The course is open to students with admission to study at the UiB. The course has 200 study places. Students who have this course as a compulsory part of their study plan will have priority access.
Teaching and learning methods
Lectures, seminars and data labs, normally 2 + 2 hours per week for 12-15 weeks.
Compulsory Assignments and Attendance
Participation: compulsory attendance at labs (at least 75%).
Approved compulsory requirements are valid for the two following semesters.
Forms of Assessment
- Group assignment where students demonstrate their ability to analyze and design machine learning solutions (30% of grade)
- 2 hour digital home exam (70% of grade) - multiple choice exam
Grading Scale
The grading system has a descending scale from A to E for passes and F for fail.
Assessment Semester
Assessment only in teaching semester.
Course Evaluation
All courses are evaluated according to UiB's system for quality assurance of education.
Examination Support Material
All written material in paper form is allowed on the exam.