The course aims to deepen the knowledge of machine learning that students have acquired in their undergraduate studies. Students learn about the most successful approaches and delve deeper into them to understand how they work and what their limitations are. The course prepares the
student for further, more in-depth study of machine learning approaches and also for the application of machine learning methods in practice.

The course will cover the following topics:

• What is machine learning, what are the basic principles, what are we trying to achieve.
• Linear regression and regularisation, loss functions.
• Model evaluation.
• Gradient descent and stochastic gradient descent and why they are useful in machine learning.
• Logistic regression.
• Generalised linear models.
• Ensemble methods.
• Kernel methods.
•  Artificial neural networks.
• Dimensionality reduction methods.
• Explainable machine larning methods
• Reinforcement learning   

Labs

The labs will consist of independent problem-solving on the topics covered during the lectures. The assistants will briefly explain the theory behind the assignment and what students have to do. Students will then work independently on the assignments. The solutions to the assignments will have to be defended. If students complete the assignment during the labs, they can defend it immediately or they can wait for the next labs. The last date for the defense will be during the "defense week". There will be a "defense week" every three weeks where students will be able to defend the previous three labs.

• Computer and Information Science