FYS-2021 Machine Learning - 10 ECTS
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