XP36RGM -- Reading group in data mining and machine learning

Data mining aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. Data size and data heterogeneity make two key data mining technical issues to be solved. The main goal is to understand the patterns that drive the processes generating the data. Machine learning focuses at computer algorithms that can improve automatically through experience and by the use of data. It often puts emphasis on performance that the algorithms reach. The distinction between DM and ML is not strict as machine learning is often used as a means of conducting useful data mining. For this reason, we cover both the areas in the same course.

The course will take a form of reading and discussion group. Each student gives two 1 hour lectures, followed by a 30 min discussion. One of the lectures should be general (MMDS book chapters, recent tutorials at major ML/DM conferences, etc.), the second one can present your research (if ML/DM related) or a ML/DM topic that is closely related to your research or research interests. Each student is supposed to read a review paper recommended for the topic before presentations of the other students. It is assumed that students have completed at least some of the master courses on Machine Learning and Data Analysis (B4M36SAN, B4M46SMU, BE4M33SSU).

Go beyond the literature, provide own insight, offer own illustrative examples, etc.

The students who do not present are supposed to read recommended reading and prepare a couple of questions before the class. The questions will be discussed during or after the talk.

Fall 2021

L Date Presents Contents Reading Talk, other links
1 Oct 1 JK, FZ Course overview, introduction, research interests. Course overview
2 Oct 15 Xuzhe Dang Model-based reinforcement learning. MuZero
3 Oct 22 Peter Jung How neural networks changed the landscape of graph processing. GNNs
4 Nov 5 Herbert Ullrich Automated Fact-Checking. AFC
5 Nov 5 Jaroslav Moravec Outlier detection methods – RANSAC. TBD
x Jan ? JK, FZ exam


Evaluation, requirements

  • every student must give his talks (the principle requirement in this type of course),
  • attendance and active discussion at presentations of other students,
  • pass the exam, i.e., prove the knowledge of basic concepts presented during the course.

Previous runs

  • XP36VPD – Selected parts of data mining, a very similar content (despite its title it covered ML topics too), running since 2015.
courses/xp36rgm/start.txt · Last modified: 2021/10/15 15:23 by klema