This course gives a fundamental introduction to machine learning (ML) with an emphasis on statistical aspects. In the course, we focus on both the theoretical foundation for ML as well as the application of ML methods.
The course enables students to analyse machine learning algorithms, implement abstractly specified machine learning methods in an imperative programming language, modify machine learning methods to analyse practical datasets and convey the results.
The subjects covered include linear models for regression and classification, neural networks, kernel methods and graphical models.
- Linear models for regression and classification
- Neural networks
- Kernel methods
- Graphical models
- Mixture Models and EM
- Continuous Latent Variable
- Models for Sequential Data
- Ensemble methods
Intended learning outcomes
After the course, the student should be able to:
- Discuss, clearly explain, and reflect upon central machine learning concepts and algorithms.
- Choose among and make use of the most important machine learning approaches in order to apply (match) them to practical problems.
- Implement abstractly specified machine learning methods in an imperative programming language.
- Combine and modify machine learning methods to analyse practical dataset and covey the results.
Ordinary examExam type:
D: Submission of written work with following oral, external (7-trinsskala)
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted. The group has a shared responsibility for the content of the report.
The group makes their presentations together and afterwards the students participate in the dialogue individually while the rest of the group is outside the room. (Mixed exam 2). The exam will last 20 min per students. The groups must consist of 2-3 persons.