This page is located in archive. Go to the latest version of this course pages. Go the latest version of this page.

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 2019

L Date Presents Contents Reading Talk, other links
1 Oct 11 JK, FZ Course overview, introduction, research interests. Course overview
2 Oct 18 Anh Vu Le Non-linear dimensionality reduction – UMAP DR review UMAP, tSNE, Talk
x Oct 25 Cancelled
3 Nov 1 Petr Marek Text classification with transformers The Illustrated Transformer Transformers, Illustrated Bert, Illustrated GTPT-2, Talk
4 Nov 8 Petr Tomasek Solving imperfect information games Deepstack Decision-making in large two-player zero-sum games, Talk
5 Nov 15 Dominic Seitz Variational autoencoders VA Tutorial Auto-Encoding Variational Bayes, VA Comprehensive Tutorial, Talk
6 Nov 22 Michal Bouska Recurrent neural networks LSTM tutorial LSTM paper, Pointer networks, Talk
7 Nov 29 Jianhang Ai Quantum computing and machine learning Quantum ML QC videos,Math in QC video, Talk
8 Dec 6 Milos Pragr Incremental learning Incremental learning Dataset shift, LWPR algorithm, Talk
9 Dec 13 Anh Vu Le Partial least squares in Alzheimer disease research PLS Talk
9 Dec 13 Petr Marek Intent Classification and Out-of-Scope Prediction Intent Classification Talk
x Dec 20 Cancelled
10 Jan 10 Nela Grimova Recent advances in active learning AL Survey Talk
11 Jan 17 Petr Tomasek Is continual resolving really almighty? PAWS Talk
11 Jan 17 Dominik Seitz Variational Graph Auto-Encoders VGAE, GCN Talk
12 Jan 24 Jianhang Ai Concentration inequalities of U-statistics for sampling without replacement Concentration inequalities Talk
12 Jan 24 Michal Bouska Heuristic Optimizer using Regression-based Decomposition Algorithm CombGraphs Talk
x Jan 31 Cancelled
13 Feb 7 Milos Pragr Gaussian Processes in Robotic Modeling GPOccupancy Talk
13 Feb 7 Nela Grimova Machine learning from biosignals
13 Feb 7 JK, FZ zkouška


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: 2020/02/07 12:20 by klema