lecture on
Advanced Data Mining

TBA

REMARK:  A presentation is not a book, notes, or script – it is rather a summary of the material covered, a demonstration of selected algorithms, and a presentation of selected examples. Therefore, reviewing the presentations alone may not be enough to understand the entire lecture. We strongly encourage you to attend the lecture regularly!

Lecture 1:  Self-supervised Learning [PDF]
Reading:
https://arxiv.org/abs/1803.07728

Lecture 2:  Self-supervised Learning [PDF]
Reading:
https://proceedings.mlr.press/v119/chen20j.html
https://arxiv.org/abs/1911.05722
https://arxiv.org/abs/2006.07733
https://arxiv.org/abs/2104.14294
https://proceedings.mlr.press/v119/wang20k.html

Lecture 3: Transformers and Attention [PDF]

https://www.youtube.com/watch?v=T3OT8kqoqjc

Lecture 4: GNN [PDF]

https://gnn.seas.upenn.edu/lectures/

Lecture 5: Intro to RS [PDF]
Reading:
https://medium.com/data-science/introduction-to-recommender-systems-6c66cf15ada
https://www.evidentlyai.com/ranking-metrics/mean-reciprocal-rank-mrr
https://www.evidentlyai.com/ranking-metrics/ndcg-metric
https://proceedings.neurips.cc/paper_files/paper/2007/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf
https://www.auai.org/uai2009/papers/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdf
Topics for Mini-Talks (open till 28.04):
https://arxiv.org/abs/2309.12858
https://arxiv.org/abs/1811.00855

Lecture 6: Advanced MF techniques for RS [PDF]
Reading:
https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf
https://arxiv.org/abs/1507.08439
https://dl.acm.org/doi/10.1145/3038912.3052569
Lecture 7: Modern RS - interactions as sequences and graphs [PDF]
Recall before the lecture:
- GRU
- Transformer
- basic GNN,  graph convolutional netwrok
Reading:
https://arxiv.org/abs/1808.09781
https://arxiv.org/abs/2002.02126
https://dl.acm.org/doi/10.1145/3404835.3462862
https://dl.acm.org/doi/10.1145/3627673.3679655
https://dl.acm.org/doi/10.1145/3477495.3531937
Next mini-talk slot: 21.04.2026 

1.  Assignment 1 [IPYNB], additional materials [ZIP] (5 points, deadline: March 2, 2026)

2. Assignment 2 [PDF] (10 points, deadline: March 9, 2026) -- alreade updated :)

3. Assignment 3 [PDF] (5 points, deadline: March 24, 2026)

4. Assignment 4 [PDF] (5 points, deadline: March 24, 2026)

5. Assignment 5 [PDF] (5 points, deadline: April 14, 2026) last update: 30.03.2026 14:49

6. Assignment 6 [PDF] (10 points, deadline: May 4-5, 2026)