Learning Systems, 7.5 credits

Main field of study with advanced study

Bachelor of Science degree (or equivalent) in an engineering subject or in computer science. Courses in computer science, computer engineering or electrical engineering of at least 90 credits, including thesis. Courses in mathematics of at least 30 credits or courses including calculus, linear algebra and transform methods. The course Engineering mathematics 7.5 credits. Exemption of the requirement in Swedish is granted. English 6.

The course is included as an elective course in the Programme of Computer Science and Engineering (300 credits), Master's Programme (120 credits) in Embedded and Intelligent Systems and the Master's Programme (120 credits) in Information Technology. The course is also offered as a freestanding course.

The course aims at providing an overview of the machine learning field; learning and self-organizing systems for classification and prediction.

Following successful completion of the course the student should be able to:

Knowledge and understanding

• describe basic linear machine learning algorithms
• describe basic nonlinear machine learning algorithms
• describe main application areas of machine learning algorithms

Skills and ability

• apply machine learning methods on real world problems
• present scientific results in the learning systems area

Judgement and approach

• assess when and which machine learning methods are applicable
• analyze and explain scientific results from the machine learning area.

Overview of learning systems. Overview of classification and regression. Overview of products on the market and common application areas for learning systems. Important aspects and standard methods in learning systems. The most common techniques and models for learning systems will be introduced e.g., artificial neural networks.

Instruction consists of lectures and practical labs. In lectures, scientific methods are presented and discussed. In the labs the student solves practical problems using machine learning methods.

Examination is by approved labs, and a final written exam.

If there are special reasons, the examiner may make exceptions from the specified examination format and allow a student to be examined in another way. Special reasons can e.g. be study support for students with disabilities.

Course evaluation is part of the course. This evaluation offers guidance in the future development and planning of the course. Course evaluation is documented and made available to the students.