Attention [COVID Requirements for participation: "3G" rule]
The lecture Mathematical Introduction to Machine Learning held
by Dr. rer.-nat. Igor Bjelakovic will start this semester on Thursday, 28th of October 2021. The course takes place as hybrid lecture every Thursday in the room MAR 0.001 & via zoom
from 12:00- 14:00 (c.t.). The last lecture will be on 17th of February 2022.
- Study program
- Matriculation number
- Desired participation (online or in presence)
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