[COVID Basic Protective Measure: https://www.berlin.de/corona/massnahmen/verordnung/]
The lecture Mathematical Introduction to Machine Learning held
by Dr. rer.-nat. Igor Bjelakovic will start this semester on Wednesday, 19th of October 2022. The course takes place as presence lecture every Wednesday from 12:00 - 14:00 (c.t.) in the HFT-TA building room 131. The last lecture will be on 15th of February 2023.
In
order to complete the first module (Mathematics of Machine Learning),
the lecture Theory and Algorithms of Machine Learning for Communication
has to be taken additionally in the summer semester term (held by Prof.
Dr.-Ing. Slawomir Stanczak).
In order to complete the
second module (Modern Signal Processing for Communications), the lecture
Modern Signal Processing for Communications has to be taken
additionally in the summer semester term (held by Dr.-Ing. Renato L.G.
Cavalcante).
After
completing both lectures you will be tested in one oral exam
and will receive 6 credit points. Please ask for an examination date at
sekretariat@netit.tu-berlin.de. The registration for the module will be executed at the Examination Office
(Prüfungsamt). You will receive a yellow sheet --> bring it to the
exam (without sheet, no exam!)
Information for exchange students: it is possible to be examined only in one lecture of the module with 3 Credit Points. You do not need to register at the Examination Office, simply ask for an examination date at sekretariat@netit.tu-berlin.de. After passing the exam you receive a certificate.
Recommended Literature
- S. Shalev-Schwartz and S. Ben-David: "Understanding Machine Learning: From Theory to Algorithms", Cambridge University Press, 2014
- M. Mohri, A. Rostamizadeh, A. Talwalker. “Foundations of Machine Learning”, MIT Press, 2018
- R. Vershynin, “High-Dimensional Probability: An Introduction with Applications in Data Science”, Cambridge University Press, 2018
- M. Wainwright, “High-Dimensional Statistics: A Non-Asymptotic Viewpoint”, Cambridge University Press, 2019
- P. Rigollet: Mathematics of Machine Learning, MIT (https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/lecture-notes/)
- John C. Duchi, "Introductory Lectures on Stochastic Optimization", Stanford University (https://web.stanford.edu/~jduchi/PCMIConvex/Duchi16.pdf)
- Trainer/in: Igor Bjelakovic
- Trainer/in: Kerstin Reinhardt
- Trainer/in: Slawomir Stanczak