A Machine Learning Theory Dream Team

Russia is the birthplace of many great mathematicians, and the work from quite a few of them have significant impact on state-of-the-art computer science and machine learning theory, e.g.:

• Ludwig O. Hesse (1811-1874) - Hessian Matrix
• Used in calculations of Logistic Regression (which can be used for binary classification), and feed-forward Neural Networks

• David Hilbert (1862-1943) - Hilbert Space
• E.g. a feature space for Radial Basis Function kernels in Support Vector Machine classifiers can be described with a Hilbert Space

• Andrey N. Kolmogorov (1903-1987) - Kolmogorov Complexity
• Used in algorithmic information theory, and also in theory behind evolutionary and genetic programming

• Andrei A. Markov (1856-1922) - Markov Models and Markov Chains
  
• Can be used e.g. for simulation (in games).
  
• Noteworthy later "spinn-offs": Hidden Markov Models (HMM) and Markov Chain Monte Carlo (MCMC).

• Andrei N. Tikhonov (1906-1993) - Tikhonov Regularization
• Tikhonov Regularization is roughly a templating language for classification and regression. The template variable is a loss function, e.g. if using a square loss function you get Ridge Regression (also known as Regularized Least Squares Regression or Shrinkage Regression), an epsilon-insensitive loss function gives Support Vector Machine Regression, and a hinge loss function gives Support Vector Machine Classification.

Kudos.