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 🔓
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