XP36RGM -- Reading group in data mining and machine learning

Data mining aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. Data size and data heterogeneity make two key data mining technical issues to be solved. The main goal is to understand the patterns that drive the processes generating the data. Machine learning focuses at computer algorithms that can improve automatically through experience and by the use of data. It often puts emphasis on performance that the algorithms reach. The distinction between DM and ML is not strict as machine learning is often used as a means of conducting useful data mining. For this reason, we cover both the areas in the same course.

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 should be general (MMDS book chapters, recent tutorials at major ML/DM conferences, etc.), the second one can present your research (if ML/DM related) or a ML/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. It is assumed that students have completed at least some of the master courses on Machine Learning and Data Analysis (B4M36SAN, B4M46SMU, BE4M33SSU).

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 2023

L	Date	Presents	Contents	Reading	Talk, other links
1	Oct 6	JK	Course overview, introduction, research interests		Course overview
2	Oct 20	Alikhan Anuarbekov	Large language models and their learning applications	LLMs	Talk
3	Nov 3	Fadwa Idlahcen	Causal inference	CI	Talk
4	Nov 24	Jiri Nemecek	Active learning: the machine learning kind	DeepAL	Talk
5	Dec 8	Alikhan Anuarbekov	Graph neural networks	GNNs	Talk
6	Jan 5	Fadwa Idlahcen	Trustworthy AI	TrustAI	Talk
7	Jan 19	Jiri Nemecek	Interpretable ML	IntML	Talk
7	Jan 19	JK	exam directly follows the last talk

References

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: 2023, KDD: 2023, ECML/PKDD: 2023, NeurIPS: 2023,
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.

Previous runs

XP36VPD – Selected parts of data mining, a very similar content (despite its title it covered ML topics too), running since 2015.

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