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 2024

L	Date	Presents	Contents	Reading	Talk, other links
1	Sept 27	JK	Course overview, introduction, research interests		Course overview
2	Oct 11	Muris Sladic	Large Language Models – Training, Fine-Tuning, and Applications	LLMs	Talk
3	Oct 25	Martin Krutský	Explaining graph-neural networks	eGNNs	Talk
4	Nov 1	Armin Hadzic	Efficient and Effective Model Extraction	EEME	Talk
5	Nov 8	Jakub Peleska	Multimodal Machine Learning	MML	Talk
6	Nov 15	Martin Rektoris	State-space models for sequence modelling	SSMSM	Talk
7	Nov 22	Muris Sladic	Time-series prediction with reservoir computing	RC	Talk
8	Nov 29	Martin Krutský	Solving Combinatorial Optimization Problems with GNNs	piGNNs	Talk
9	Dec 6	Armin Hadzic	Probabilistic sufficient explanations	PSEs	Talk
10	Dec 13	Jakub Peleska	Graph Neural Networks for Relational Databases	RDL	Talk
11	Jan 10	Martin Rektoris	Deep generative models and probabilistic inference	DGMs	Talk
11	Jan 10	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 deliver his presentations (the primary requirement for this type of course),
attendance and active participation in discussions during the presentations of other students,
pass the exam, i.e., demonstrate knowledge of the basic concepts covered in 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.

Table of Contents