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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 Talk
3 Oct 22 Peter Jung How neural networks changed the landscape of graph processing GNNs Talk
4 Nov 5 Herbert Ullrich Automated Fact-Checking AFC Talk
5 Nov 5 Jaroslav Moravec Outlier detection methods – RANSAC USAC Talk
6 Nov 12 Michaela Urbanovska Neural Algorithmic Reasoning NAR Talk
7 Nov 19 Ondrej Lukas Explainable AI ExpNN Talk
8 Nov 26 Lukas Korel ML with ontologies MLwO Talk
9 Dec 3 Peter Jung Generative Adversarial Networks GANs Talk
10 Dec 10 Xuzhe Dang StyleGANs StyleGANs Talk
11 Dec 17 Herbert Ullrich BERT-like encoder models BERT TempTalk,Demo
12 Jan 7 Ondrej Lukas Federated learning FederatedAveraging Talk
13 Jan 14 Jaroslav Moravec Visual space odometry VSO TempTalk
14 Jan 21 Michaela Urbanovska Deep learning for automated planning Geffner Talk
15 Jan 28 Lukas Korel Video Scene Location Recognition ISR
x Jan 28 JK, FZ exam – see the plan in the table below

Final presentations = exam

Each participant prepares a 5-7min talk that summarizes the main ideas presented before by another course participant. The topic assignment is:

Topic Date of the original presentation For exam presented by
Model-based reinforcement learning Oct 15 Lukas Korel
How neural networks changed the landscape of graph processing Oct 22 Herbert Ullrich
Outlier detection methods – RANSAC Nov 5 Xuzhe Dang
Neural Algorithmic Reasoning Nov 12 Peter Jung
Explainable AI Nov 19 Jaroslav Moravec
Generative Adversarial Networks Dec 3 (Dec 10 could be used too) Michaela Urbanovska
BERT-like encoder models Dec 17 Ondrej Lukas


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: 2022/01/27 17:17 by klema