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 2016

Meetings every Thursday at 11:00 in 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 Karel Horák Learning to rank learningtorank.pdf
Oct 13 Martin Svatoš Relational frequent pattern mining vpd_frpm.pdf
3 Oct 20 Petr Ryšavý Clustering of biological sequences bioclustering.pdf
Oct 20 Jan Mrkos Multicriterial learning (clustering) multicriterial.pdf
4 Oct 27 Petr Váňa Reinforcement learning in robotics rl-vana.pdf
Oct 27 Martin Matyášek (Deep) reinforcement learning deep-rl.pdf
5 Nov 3 Martin Matyášek AlphaGO, a deep RL application in games deep-rl-all.pdf
Nov 3 Petr Čížek Managing and mining (streaming) sensor data stream_mining_cizek.pdf
6 Nov 10 Jáchym Barvínek Learning structure-activity models qsar.pdf
Nov 10 Jan Šimbera Non linear dimensionality reduction dimred.pdf
7 Nov 17 Holiday
8 Nov 24 Filip Paulů Data mining applications in manufacturing cancelled
Nov 24 Marek Cuchý Mining social networks
9 Dec 1 Karel Horák Deep learning: methods and applications deeplearning.pdf
Dec 1 Martin Svatoš Rule extraction from neural networks vpd_ruleextraction.pdf
10 Dec 8 Petr Ryšavý Recommender systems recommendersystems.pdf
Dec 8 Petr Čížek Learning from various sensory modalities sensory.pdf
11 Dec 15 Jan Mrkos Non-convex optimization in machine learning non-convex.pdf
12 Jan 5 Jáchym Barvínek Abductive logic programming for metabolic network learning abductive.pdf
Jan 5 Jan Šimbera Machine learning in geography geo.pdf
13 Jan 12 Marek Cuchý Advertising on the Web
Jan 12 Petr Váňa Reinforcement learning in robotics, part II
Jan 12 JK, FZ zkouška

References

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.
 
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courses/xp36vpd/start.txt · Last modified: 2017/01/12 14:23 by klema