Srđan Lazendić
Postdoctoral Assistant and Researcher
Lecturer: Hennie De Schepper
Teaching language: Dutch
Content:
The aim of this course is threefold: (1) to rehearse the fundamental mathematics knowledge for starting academic engineering studies;
(2) to make clear what is the expected mathematics level in academic engineering studies; (3) to establish a uniform starting level
for all students.
Lecturer: Hennie De Schepper
Teaching language: Dutch
Content:
The main aim is to teach the student how to reason in a critical, logical and structured
way, on the appropriate level of abstraction, while paying attention also to
completeness and precision.
Lecturer: Hendrik De Bie
Teaching language: Dutch
Content:
The student gains insight in solution methods for ordinary and partial differential
equations. The student also gains insight in the definition of line and surface integrals,
the underlying theoretical results and their practical computation.
Lecturer: Marián Slodička
Teaching language: Dutch
Content:
The intention of this course is to give the students an introductory but efficient matter
consisting of terms, results and methods from functional analysis.
This will be illustrated on some simplified practical applications coming from various
engineering disciplines.
The fundamental concepts of the operator and spectral theory will be explained and
applied to diverse problems.
Lecturer: Gert de Cooman
Teaching language: Dutch
Content:
The intention of this course is to teach the students the basic principles and the reasoning methods of probability
theory. Furthermore, we want to familiarise them with the most commonly used probabilistic and statistical techniques,
and the ideas behind them. Finally, we teach them how to apply probabilistic and statistical methods using pen-and-paper
methods and in Python.
Lecturer: Aleksandra Pižurica
Teaching language: English
Content:
The course gives an overview of the principles and modern approaches in artificial intelligence.
The focus is on intelligent agents, reasoning under uncertainty, and making rational decisions. It covers the following topics: knowledge representation,
reasoning under uncertainty, Bayesian networks, Hidden Markov Models, belief propagation, deep learning, rational agents and rational decisions as well as
visual intelligence.