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

References

Rajaraman, A., Leskovec, J., Ullman, J. D.: Mining of Massive Datasets, Cambridge University Press, 2011.
Free Data mining Books
Recent tutorials, major ML/DM conferences: KDD2014, DS2014, ICML16, ECML16, NIPS16
Review papers: Yang, Wu: 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH, Wu et al.: Top 10 algorithms in data mining
External seminars: ML seminars at MFF, PIS, Machine Learning Meetups, FIS KEG.

Links

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.

Table of Contents

xp36vpd -- Selected parts of data mining

Fall 2017

References

Links

Evaluation, requirements