FYS-2021 Machine Learning - 10 ECTS
Generell studiekompetanse eller realkompetanse + SIVING
Local admission, application code 9391 - enkeltemner i ingeniørfag.
Course contentThe course will introduce the students to the fundamental concepts in machine learning and will study widely used and popular machine learning algorithms for analysing data in the modern society. The course will cover elementary methods for both supervised and unsupervised learning, both for regression and classification. Supervised methods will include technologies such as decision trees, linear discrimination and neural networks. TUnsupervised methods covered will include machine learning methods based on linear algebra as well as standard clustering methods. The course will have a significant practical component, in which various applications will be treated in the form of case studies.
Objectives of the course
Knowledge - The student is able to:
- Describe fundamental concepts behind machine learning in modern society.
- Describe a number of machine learning application areas in society.
- Discuss and select appropriate data sources applicable to a given machine learning approach.
- Discuss and select appropriate approaches, such as unsupervised versus supervised, when it comes to the choice of machine learning algorithm to use.
Skills - The student is able to:
- explain the application domains of machine learning methodology and machine learning algorithms for data analysis in society and research.
- analyse data for knowledge extraction and inference by applying various machine learning methods and algorithms.
General expertise - The student is able to:
- understand the role of machine learning in modern society in the context of data analysis
- implement and apply fundamental machine learning methods and algorithms for analysis of data in e.g. Matlab or Python
- 2 mandatory assignments
Access to the home exam requires passing the first mandatory assignment and access to the final exam requires passing both mandatory assignments.
- A home examination, counting 40%
- 4 hours written examination, counting 60%
Assessment scale: Letter grades A-F. A is the highest grade and F is failed.
A re-sit examination will not be arranged in this course
See chapter 5 in Regulations for studies and examinations at the UiT The arctic university of Norway for more information
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: FYS-2021
- Responsible unit
- Institutt for fysikk og teknologi
Robert JenssenProfessor / Maskinlæring / Senterleder Visual Intelligence
- Tidligere år og semester for dette emnet