ETE391 · 4 credits · Basic level (G2F) · Informationsteknologi, Datateknik, Datavetenskap, Elektroteknik, Medicinsk teknik
Machine Learning
Active · next start HT 2026
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About the course
Aim
The aim with the course is that the student should understand different paradigms and methods for machine learning and be able to implement and use such methods for different kinds of data analysis.
After the course, the student should be able to:
- Describe the difference between different machine learning paradigms
- Implement and use some of the common methods withing these paradigms
- Select the proper method for a given problem
Content
- Different machine learning paradigms
- Classification / Regression
- Supervised learningLinear classifiersNeural networksDeep learningConvolutional neural networks
- Unsupervised learningPrincipal component analysisClustering
- Reinforcement learningQ-learning
- Examiner
- Martin Hultman
- Scheduled hours
- 16 h
- Main field of study
- Informationsteknologi, Datateknik, Datavetenskap, Elektroteknik, Medicinsk teknik