1. Foundations of Machine Learning and Data Mining @TUHH 2013 1 Rainer Marrone, Ralf Möller

2. What‘s missing? Knowledge in Learning (AIMA Ch. 19)  Learning based on first-order logic  Learning based on first-order probabilistic logic (e.g., PRMs) Learning for continuous models (AIMA Ch. 20)  Gaussian ML (20.2.3/4)  Density estimation with nonparametric models only sketched (20.2.6)  Mixture of Gaussians (20.3.1) Learning Hidden Markov Models or Dynamic Bayesian Networks (20.3.3)  But see Reinforcement Learning Some techniques only sketched...  e.g., Neural networks  e.g., Support vector machines (SVMs) 2

3. Further Reading 3

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