Learning Systems
The course aims at providing an overview of the field machine learning; learning and self-organizing systems for classification and prediction.
Upon completion of the course, the student shall be able to
- judge when the methods introduced in the course is applicable
- read and comprehend scientific material in the area
- apply the methods on real world problems
- assimilate and present scientific results in the learning systems area
Level:
Advanced level
Application code:
X3432
Entry requirements:
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.
Selection rules:
Available for exchange students. Limited numbers of seats.
Start week:
week: 03
Instructional time:
Daytime
Language of instruction:
Teaching is in English.
Level:
Advanced level
Application code:
X3432
Entry requirements:
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.
Selection rules:
Available for exchange students. Limited numbers of seats.
Start week:
week: 04
Instructional time:
Daytime
Language of instruction:
Teaching is in English.
