back to list

course: 2AMM20

Objectives

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.

It is expected that students already mastered the foundations of data mining and machine learning and know how to use data mining and machine learning libraries. It is highly recommended to have programming experience.

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.



Details
Level:
Master
Quartile:
Q1
Lecturers:
Wouter Duivesteijn
Pratik Gajane
Rianne Schouten
Stiven Schwanz Dias