Syllabus
1. Statistical models of machine learning.
2. Supervised learning, unsupervised learning.
3. Generalization ability of machine learning.
4. Neural networks and deep learning.
5. Bayesian machine learning and Bayesian networks.
Annotation
In Machine Learning one develops mathematical methods for modeling data structures, which express dependency between observables, and designs efficient learning algorithms for estimation of such dependency.
The most advanced part of Machine Learning is statistical learning theory that takes into account our incomplete information of observables, using probability theory, or preferably, using measure theory and functional analysis. In this way we not only unveil hidden structure of data but also make a prediction for the future.