After completing this course successfully, the student can:
- Describe and compare basic machine learning methods and techniques, associated complexity and the application domain. Theories and techniques include: Bayesian classification theory, Decision Trees, Linear Discrimination, Neural Networks, Support Vector Machines.
- Design, implement and systematically evaluate machine learning methods and models.
The course is an in-depth introduction to the theory and practicalities of Machine Learning (ML), in which the emphasis is on an overview of the various techniques, their workings, associated complexity and application domains. We also look into the theoretical aspects of machine learning techniques, such as over- and under-fitting and the Bias/Variance Dilemma. Emphasis is on basic ML models, on methodology (how to achieve reliable models systematically) and the evaluation of the learnt/trained models. |
- Working knowledge in probability theory and statistics. This is covered in the course Probability Theory, or equivalent.
- Basic working knowledge in linear algebra
- Knowledge about basic AI formalisms for defining and solving problems: search, representation of knowledge, reasoning, learning and reasoning under uncertainty. This is covered in the bachelor's course Artificial Intelligence (202000993).
Machine Learning, Supervised Learning, Bayesian Decision Theory and Models, Parametric Models, Decision Trees, Linear Discrimination, Neural Networks, Support Vector Machines, Evaluation of Classifiers.