Advanced Machine Learning
This is a complete 15 ECTS course on Machine Learning. Building on the math knowledge acquired from the course Linear Algebra and Probability, students will be introduced to Machine Learning during the first part of the course. In the second part, recent machine learning research will be addressed.
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.
Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; deep learning; reinforcement learning; kernel machines; clustering; graphical models; Bayesian estimation; ensemble methods, and statistical testing.
Linear Algebra and Probability
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, Internal (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.