AbstractThis 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
- Decision theory
- Mixture Models and EM
- Ensemble methods
- Decision trees
- The prerequisite required for admission to the course is Linear Algebra and Probability or similar.
- Some prior experience with Python will be assumed.
- It is recommended to have a basic knowledge of statistics.
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 convey the results.
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.