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.

• Recent papers: Distill papers, Optimization Methods for Large-Scale Machine Learning, Wasserstein GAN, XGBoost: A Scalable Tree Boosting System,Deep Forest, Quantum Machine Learning,
• 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: ICML 2020, ICML 2019, KDD 2020,KDD 2019, ECML/PKDD 2020, ECML/PKDD 2019, NeurIPS 2019, NIPS 2018
• 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.

• Lecturers: Jiří Kléma, Filip Železný
• Class schedule, meetings every Friday at 10:30 in MS Teams. NOT as in the official schedule!
• Course syllabus.

• 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.