Machine Learning

Level of Study

Bachelor

Teaching semester

Spring

Objectives and Content

The course introduces Machine Learning, with a view towards data analysis applications. Topics covered are supervised learning (classification and regression), unsupervised learning including clustering, decision tree learning, Bayesian learning, and working with textual data.

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

Required Previous Knowledge

INFO132 or equivalent. Basic understanding of programming and algorithms.

Recommended Previous Knowledge

INFO132 or equivalent. Basic understanding of programming and algorithms.

Access to the Course

The course is open to all students at the University of Bergen.

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 seminars and labs (at least 80%).

Forms of Assessment

• Group assignments in the same groups (70%)
• Individual assignments (30%)

Grading Scale

The grading system has a descending scale from A to E for passes and F for fail.

Assessment Semester

Spring