The lecture Theory and Algorithms of Machine Learning for Communication held by Prof. Dr.-Ing. Slawomir Stanczak will start this semester on Monday, April, 13th 2026. The course takes as presence lecture every Monday from 12:00 to 14:00 (c.t.) in the HFT-TA building room 131 . The last lecture will be on July,13th 2026.
Theory and Algorithms of Machine Learning for Communication is part of the module Mathematics of Machine Learning. In order to complete the whole module with 6 Credit Points, the students must also cover the lecture Mathematical Introduction to Machine Learning which is offered every winter term by Dr. rer. nat. Igor Bjelakovic.
After completing both lectures the students will be tested in one oral exam or in two separated oral exams. Please ask for an examination date at sekretariat@netit.tu-berlin.de.
Please note that you will only receive CP´s for the full completed module. Only for exchange students the following regulation applies:
The registration for the module will be proceeded at the Examination Office (Prüfungsamt).
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.
Please provide the following information in the e-mail for the exam registration:
- Matriculation number & degree program
- Exchange student (yes/ no)
- Should the examinations take place in two parts or one examination?
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Recommended literature: |
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P. Rigollet: Mathematics of Machine Learning, MIT Lecture Notes (online) |
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R. Vershynin: High-Dimensional Probability: An Introduction with Applications in Data Sciences (book in preparation, online) |
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S. Shalev-Schwartz and S. Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014 |
Please note:
The lecture notes of this course are exclusively for the personal use of the enrolled students. Any type of distribution, reproduction, disclosure, and non-personal usage is prohibited.
Moreover, recording the lectures in any form (including audio and video), even partially and for private use, is strictly forbidden.
All data, especially personal and confidential data, must be protected by the student in such a way that unauthorized access and unauthorized access to the data and documents is effectively prevented. Data, information or documents may not be passed on to third parties nor may they be made available for inspection by third parties (e.g. on screen or on printouts). The student is responsible for compliance with this principle and for the possible consequences of non-compliance.
- Trainer/in: Kerstin Reinhardt
- Trainer/in: Ine Scharse
- Trainer/in: Slawomir Stanczak