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Høst 2024
FYS-2021 Machine Learning - 10 stp
The course is administrated by
Institutt for fysikk og teknologi
Type of course
The course is available as a singular course. The course is also available to exchange students and Fulbright students.
Course contents
The 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.
Admission requirements
Generell studiekompetanse eller realkompetanse + SIVING
Local admission, application code 9391 - enkeltemner i ingeniørfag.
Objective 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
Language of instruction
The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.
Teaching methods
Lectures: 30 hours
Exercises : 30 hours