This course teaches the basics of programming in Python. In addition to syntax of Python, it also introduced you to the core concepts of programming: sequence, repetition, selection, typing and functions. Finally it explains how to use a modern IDE (Interactive development environment) to program.

Dit vak maakt studenten vertrouwd met statistiek: de wetenschap die zich bezighoudt met het beschrijven en analyseren van gegevens, met de bedoeling daaruit algemeen geldende conclusies te kunnen trekken. De nadruk ligt op het begrijpen en correct toepassen van statistische methoden, niet op de wiskundige verantwoording van die methoden.
We beschouwen zowel beschrijvende statistiek (methoden voor het beschrijven van een verzameling gegevens) als inferentiële statistiek (methoden voor het afleiden van eigenschappen van een niet volledig geobserveerde populatie uit een representatieve steekproef).
De cursus introduceert de vereiste basisconcepten (toevalsvariabelen, kansen, statistieken, parameters, kansverdelingen, inferentie, punt- en intervalschattingen, hypothesetesten), methoden voor inferentie van specifieke parameters (zoals gemiddelde, standaarddeviatie, correlatie, ...), hypothesetesten betreffende een of meerdere variabelen, en het construeren van voorspellende modellen (lineaire regressie).

Data mining probeert interessante patronen uit grote hoeveelheden data te halen. In dit college wordt eerst een aantal basisbegrippen uit de statistiek behandeld en daarna wordt ingegaan op de onderliggende principes van data mining algoritmen en hun toepassing (gegevens vergaren, visualisatie, data analyse en onzekerheid).

Een DataBase Management System (DBMS) vormt een onontbeerlijke tool om grote hoeveelheden gegevens te beheren. Heden ten dage komen databases voor in zo ongeveer elke branche op het gebied van wetenschap, technologie en zakenwereld. Deze cursus is oriënteert aan databases-courses van VS top universiteiten en geeft een veelomvattende inleiding in ontwerp en toepassing van databases. De cursus verschaft praktische ervaring in het design van een database en het werken met een DBMS. Tevens zullen basis theoretische onderwerpen aan bod komen. Tenslotte verschaft de cursus een eerste overzicht van recente onderwerpen op het gebied van databases zoals Data Warehousing, Data Mining en Internet database beheer.

Human Computer Interaction is concerned with man-machine interfaces. Every system equipped with a microprocessor has some kind of user interface for its operation. This is, in particularly, the case for systems that require which interaction as an essential ingredient for its normal operation: i.e. computers and computer programs. Human Computer Interaction covers various aspects of the interaction between the human operator and a computer system. In the lectures, the underlying principles for the design of the computer interface and interaction are discussed. This includes aspects of human perception, cognitive processes and memory but also subjects directly related to interface and interaction design, i.e. metaphores, widgets, windowing systems and object orientation. In the modern approach of Human Computer Interaction, the user is the pivot of the design trajectory. Design methods are based on this principle and this will be clear in discussion of problem analysis, prototyping, evaluation and usability. Recent developments in HCI are discussed in the lectures.

Natural computing is a quickly developing field dealing with models and computational paradigms inspired by nature and attempts to understand the world around us in terms of information processing. Natural computing today includes paradigms such as modelling information processing through artificial neural networks, modelling emergent behaviour resulting from the interaction of a large collection of agents in particle swarms (representing e.g., birds, insects) or spatial arrangements of cells (cellular automata), or modelling efficient search and optimization procedures such as ant colonies (finding shortest paths in a network of possibilities), simulated annealing processes (finding the optimal energy state of a crystal), and evolutionary processes (adapting a population to find the best mix of genetic material under changing environmental conditions). The course introduces the foundations of a variety of such computational paradigms, and discusses algorithmic implementations on computers as well as the analogies between these implementations and the natural model. In addition, we also present some practical application examples of such computational paradigms, such as pattern recognition, engineering optimization, simulations of fire breakouts, to name a few.

Machine learning as a subfield of artificial intelligence (AI) is the science of the design of algorithms that can learn through experience acquired from data, without being explicitly programmed. This course provides an introduction to the topic of machine learning and provides knowledge on the core concepts needed by AI scientists. Throughout the course, students will get familiar with the fundamental concepts in the design of effective machine learning algorithms, and different classes of machine learning models. They further gain the practical skills needed to apply machine learning algorithms to new problems.

Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.