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xp36vpd -- Selected parts of data mining

Data mining aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. This course focuses on two key data mining issues: data size and their heterogeneity. When dealing with large data, it is important to resolve both the technical issues such as distributed computing or hashing and general algorithmic complexity. In this part, the course will be motivated mainly by case studies on web and social network mining. The second part will discuss approaches that merge heterogeneous prior knowledge with measured data. Bioinformatics will make the main application field here. It is assumed that students have completed at least some of the master courses on Machine Learning and Data Analysis (B4M36SAN, B4M46SMU, BE4M33SSU).

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 shall be DM general (MMDS book chapters, recent tutorials at major ML/DM conferences, etc.), the second one can present your research (if DM related) or a 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.

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 2020

L Date Presents Contents Reading Talk, other links
1 Oct 9 JK, FZ Course overview, introduction, research interests. Course overview
2 Oct 23 Petr Lorenc Few-shot learning few-shot tut Talk
3 Oct 30 Martin Smolík Automated evaluation when learning complex outputs EvalBLEU Talk
4 Nov 6 Marek Dědič Learning on graph-structured data GNNIntro Talk, Demo_pdf, Demo_zip
5 Nov 13 Petr Cezner Generative Adversarial Networks GANs Talk
6 Nov 20 Vojtěch Jindra Attention is all you need Attention Talk
7 Nov 27 Petr Lorenc Sentence embedding in NLP Sentence Embedding Talk
8 Dec 4 Martin Smolík Variational autoencoders and the math behind them VAEs Talk
9 Dec 11 Marek Dědič Multi-instance learning and its use for clustering MIL (sections 1-6) Talk, Demo_pdf, Demo_zip
10 Dec 18 Petr Cezner Gaussian processes GPs Talk
11 Jan 8 Vojtěch Jindra Multi-instance learning and learning to cluster for clustering newspapers’ texts Learning2cluster Talk
13 Jan 8 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.
courses/xp36vpd/start.txt · Last modified: 2021/01/08 11:20 by klema