URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2AMM20&collegejaar=2025&taal=enDescription
This course provides an in-depth coverage of selected research topics in data mining. Furthermore, it trains future data mining and AI researchers to work with scientific literature, in order to find and understand state-of-the-art data mining techniques, and in order to systematically develop, evaluate, and communicate (to fellow researchers) novel techniques that address real-world challenges.Objectives
Short description: Data mining is one of the fast-developing areas of AI. It is built on the foundations of machine learning, algorithms, statistics, databases and other contributing fields. State-of-the-art data mining and machine learning techniques are already used in web search, speech recognition, text translation as well as in scientific discovery, healthcare and many industries. And the current state of the art in classification, clustering, pattern mining, search, optimization and other building blocks of data-driven AI is often pushed forward in an application inspired way, i.e. by meeting the practical needs of mining structured, text, graph and raw high-dimensional multimedia data that cannot be fully realized with already existing techniques.Learning objectives: The goal of this course is to train future data scientists and AI engineers to use scientific literature in order to understand the strengths and limits of the current state of the art data mining techniques, and to learn how to systematically develop novel techniques addressing some of these limitations.
Upon completion of this course you will be able to:
- understand the current state of art in selected research topics in data mining and where the cutting-edge research is going;
- summarize and interpret scientific literature to learn about novel data mining techniques;
- judge the quality and relevance of scientific literature;
- integrate information from scientific literature to find a (solution to a) research gap;
- delineate correspondence between, on the one hand, specific aspects of a knowledge gap that a research paper addresses and, on the other hand, specific sections of a scientific paper;
- design a scientific experiment to evaluate novel data mining techniques;
- write a research paper that follows scientific guidelines and can potentially be published at a data mining conference;
- efficiently deploy your skill set to collaboratively contribute to scientific knowledge while under pressure of tight time constraints.