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Aktualności / News:

Zasady / Rules:

Zasady PDF / Rules PDF

Program seminarium / Program of the Seminar:

Overview of Supervised Learning. Linear Methods for Regression (October 20, 2021, Grzegorz Ciesielski)

Linear Methods for Classification (October 20, 2021, Heorhii Karpenko)

Basis Expansions and Regularization (October 27, 2021, Paweł Grabiński)

Kernel Smoothing Methods (October 27, 2021, Jakub Urbańczyk)

Model Assessment and Selection (November 3, 2021, Bartosz Zimończyk)

Model Inference and Averaging (November 3, 2021, Mateusz Orda)

Additive Models, Trees, and Related Methods (November 10, 2021, Katarzyna Miernikiewicz)

Boosting and Additive Trees (November 10, 2021, Maria Wyrzykowska)

Support Vector Machines and Flexible Discriminants (November 17, 2021, Karol Guzikowski)

Prototype Methods and Nearest-Neighbors (November 17, 2021, Maciej Grabias)

Unsupervised Learning (November 24, 2021, Daniel Cuervo Gomez)

Random Forests (November 24, 2021, Maksymilian Zawartko)

Ensemble Learning (December 1, 2021, Jakub Kuciński)

Deep Learning in Recommender Systems (December 1, 2021, Antoni Dąbrowski)

Reinforcement Learning in Recommender Systems (December 7, 2021, Cezary Troska)

Graphical Models (December 7, 2021, Mateusz Małowiecki)

Machine Learning and Security (December 15, 2021, Beata Janiak)

Dalsze proponowane tematy / Further topics proposed:

Shapelets [PDF]

GPS Trajectory Data Mining (e.g. [PDF])

Hidden Semi Markow Models (e.g. [PDF])

Clustering/Classifying Time Series

A Comprehensive Survey of Neighborhood-based Recommendation Methods (based on Chapter 4 from [2])

Advances in Collaborative Filtering (based on Chapter 5 from [2])

Context-Aware Recommender Systems (based on Chapter 7 from [2])

Map Based Visualization of Product Catalogs (based on Chapter 17 from [2])

Literatura / Literature:

[1] Hastie, T., Tibshirani , R., Friedman, J., The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Springer, 2013.

[2] Ricci, F., Rokach, L., Shapira, B., Kantor, P., Recommender Systems Handbook, Springer, 2011.