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Marek Gagolewski
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Recommended literature for data science students (postgraduate)¶

This list is a work in progress.

Last update: 2025-02-05.

See also: the undergraduate version.

Statistics, probability, machine learning, and the like¶

Arnold, B.C., Pareto Distributions, CRC Press, 2015

Bishop, C., Pattern Recognition and Machine Learning, Springer, 2006 🔓

Bishop, C., Bishop, H., Deep Learning: Foundations and Concepts, Springer, 2024 🔓

Devroye, L., Györfi, L., Lugosi, G., A Probabilistic Theory of Pattern Recognition, Springer, 1996

Murphy, K.P., Probabilistic Machine Learning: Advanced Topics, MIT Press, 2022 🔓

Leskovec, J., Rajaraman, A., Ullman, J., Mining of Massive Datasets 🔓

Random graphs, complex networks, graph data analysis¶

Easley, D., Kleinberg, J., Networks, Crowds, and Markets: Reasoning About a Highly Connected World 🔓

Montanari, A., Mézard, M., Information, Physics, and Computation 🔓

van der Hofstad, R., Random Graphs and Complex Networks 🔓

Durrett, R., Dynamics on Graphs 🔓

Newman, M.E.J., Networks, Oxford University Press, 2018

Hamilton, W.L., Graph Representation Learning 🔓

Stanford CS224W: Machine Learning with Graphs

Natural language processing¶

Aggarwal, C.C., Machine Learning for Text, Springer, 2022

Jurafsky, D., Martin, J.H., Speech and Language Processing (3rd ed. draft) 🔓

Eisenstein, J., Natural Language Processing 🔓

Goldberg, Y., A Primer on Neural Network Models for Natural Language Processing 🔓

Stanford CS224N: Natural Language Processing with Deep Learning

MIT Natural Language Processing (6.806-864)

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In this section
  • Recommended literature for data science students (postgraduate)
    • Statistics, probability, machine learning, and the like
    • Random graphs, complex networks, graph data analysis
    • Natural language processing