Books

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Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees

Education in Machine learning has changed drastically in recent years. The main roadstones were deep learning, reinforcement learning, and now it is physics-informed or geometric deep learning. The requirements for mathematical knowledge rise, even for engineering parts. An example is differential programming techniques. Below we present bachelor and master programs for modern Machine learning. We call it Knowledge-aware machine learning.



Machine learning for beginners

Linear algebra

Optimization

Basics of probability and statistics

  • A first course in probability by Sheldon M. Ross, 2012
  • Elements of information theory by Thomas M. Cover, Joy A. Thomas, 2006
  • Probability theory by Alexandr A. Borovkov, 2006
  • Mathematical statistics by Alexandr A. Borovkov, 1999
  • Linear Statistical Inference & Its Applications by C. Radhakrishna Rao, 1967
  • Linear Models and Generalizations: Least Squares and Alternatives by C. Radhakrishna Rao et al., 2007

Bayesian statistics and inference

Functional data analysis

Discrete analysis

  • Lectures on discrete geometry by Jiří Matoušek, 2002
  • Indiscrete thoughts by Gian-Carlo Rota, 2008
  • Graph theory by Reinhard Diestel, 2017
  • Graph theory (groups and symmetries: from finite groups to Lie groups) by Reinhard Diestel, 2000

Programming

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