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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 the master course on Machine Learning and Data Analysis (A4M33SAD).

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.

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

Fall 2017

Meetings every Friday at 11:00 in KN:E-205. NOT as in the official schedule!

L Date Presents Contents Materials
1 Oct 6 JK, FZ Course overview, introduction, research interests.
2 Oct 13 Ondřej Hubáček Outcome Forecasting in Sports hubacek_pred_sport.pdf
3 Oct 20 Vladimír Kunc Data Compression by Deep Learning kunc_dl_ae.pdf
4 Oct 27 Cancelled
5 Nov 3 Jiří Kléma Statistically significant does not mean important odvracena_strana_statisticke_vyznamnosti.pdf
6 Nov 10 Jan Pichl Question Answering and Dialog Systems pichl_qa_dialog_systems.pdf
7 Nov 17 Holiday
8 Nov 24 Magda Friedjungová Introduction to Transfer Learning friedmag_tl.pdf
9 Dec 1 Jakub Repický Active Learning in Regression Tasks vpdd2017active.pdf
10 Dec 8 David Fiedler Vehicle speed prediction based on road parameters fiedlervpd.pdf
11 Dec 15 Jan Skácel Black sheep detection using remote sensing of vehicle emissions skacel_blacksheep.pdf
12 Jan 5 Vladimír Kunc Understanding Hinton's Capsule Networks capsnets.pdf
Ondřej Hubáček Gradient boosted trees gbtrees.pdf
13 Jan 12 Jan Pichl StarSpace: Embed All The Things! vpdstarspace.pdf
Magda Friedjungová Big data mining – scalable algorithms, clustering, social data cancelled
14 Jan 19 Jakub Repický Bayesian hypotheses testing vpdd2018bayesian.pdf
David Fiedler kNN – local weighting, efficiency in high-dimensional spaces and large datasets vpdknn.pdf
15 Jan 26 Jan Skácel Fighting fake news vpdFakeNews.pdf
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: 2018/01/25 09:11 by klema