This course provides an introduction into machine learning theory including mathematical foundation. It covers the following areas:

  • Is statistical or machine learning possible? (Outlook Vapnik-Chervonenkis theory, VC Dimension and Limit)
  • Statistical/Machine learning: Supervised, unsupervised, reinforcement learning, classification, regression
  • Probabilistic and non-probabilistic methods
  • Different models: 
    • Linear model
    • Kernel methods
    • Support Vector Machines
  • Bayes methods
    • Gaussian processes
    • MCMC and particle filters
  • Neural networks
  • Genetic algorithms
  • Model selection
    • Overfitting and regularization
    • Bias-Variance tradeoff, error and noise
  • Training, testing, validation o Curse of dimensionality
  • Vapnik-Chervonenki theory, Hoeffding’s lemma, VC dimension and VC inequality
  • Structural Risk Minimization (SRM), Occham’s Razor
  • Overview of selected application fields
  • Consequences of statistical learning theory
  • Philosophic implications (Are data more important than theories? Where are the limits of statistical learning theory?
  • Ethical implications for society, principal problems of algorithmic decisions (Information privacy, individual freedom, checks-and balances of citizens, corporations, states etc.)
  • Lectures are accompanied by practical exercises with respect to theories and concepts, supported by the use of appropriate software.