Special Topics in Statistics and Probability (STSP): Methodological Tools of Machine Learning (7 ECTS)

Course Code: 
6157
Semester: 
7th
Elective Courses
Διδάσκων: 

A. YANNACOPOULOS - ST. VAKEROUDIS

The course focuses on methodological tools of machine learning, such as:

• Reproducing kernel Hilbert spaces and applications

• Manifold learning, data geometry and applications

• Universal approximation theorems and applications to deep learning

• Probability Theory in high dimensions

• Gaussian processes and applications to machine learning

• Familiarization with Python

Recommended Reading:

  • Hofmann, Thomas, Bernhard Schölkopf, and Alexander J. Smola. "A tutorial review of rkhs methods in machine learning." Technical Report (2005).
  • Higham, Catherine F., and Desmond J. Higham. "Deep learning: An introduction for applied mathematicians." Siam review 61.4 (2019): 860-891.
  • Calin, Ovidiu. Deep learning architectures. New York City: Springer International Publishing, 2020 Ch. 9
  • Lecturer Notes

The course outline can be found here.