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| = Integrated Lecture “Probabilistic Modling and Inference” = | = Probabilistic Modling and Inference = |
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| || ISIS || https://isis.tu-berlin.de/course/view.php?id=34508 || | |
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| * Gaussian process and Bayesian optimization | |
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| * Generative modeling * Bayesian deep learning |
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| * Bayesian deep learning * Generative modeling * Gaussian process and Bayesian optimization |
== Enrollment / Limited number of participants == If you intend to participate, please send an e-mail to nakajima@tu-berlin.de with the title "Probabilistic Modeling and Inference Enrollment," and the main text including * Full name * Matr. no. * Affiliation (University, Department, master/bachelor) * Email address * Module name (CA, ML, or standalone (only for non-TU students)) Participation spots are assigned on a first come/first serve basis. === Students from other universities === If you are not a student at TU and want to earn credit, you have to solicit [[https://www.tu.berlin/studierendensekretariat/themen-a-z/gast-und-nebenhoererschaft/|''Nebenhörerschaft'']]: * First fill the online [[https://www.static.tu.berlin/fileadmin/www/10002460/Bewerben_und_Einschreiben/Studierendenverwaltung/Antrag_auf_Nebenhoererschaft_englisch.pdf|''form'']] with the following details on the final page. || '''Course title and type''' || Program number ||'''Number of course hours per week (SWS)''' || '''Lecturer''' || || Probabilistic Modeling and Inference || 0434 L 503 || 2 || Shinichi Nakajima || * Send the form to one of the trainers (see contact information above) to sign * Then send the signed form to Manuela Gadow (manuela.gadow at tu-berlin.de), who is authorized to sign on behalf of our dean. * After getting your signed form back send it together with your current matriculation letter ('Immatrikulationsbescheinigung') to the student registration office (nebenhoerer@studsek.tu-berlin.de). |
Probabilistic Modling and Inference
General Information
Lecture |
Tuesdays 14-16 (23.5.2023-) |
Room |
MAR 4.062 |
Teachers |
Shinichi Nakajima |
Contact |
|
Language |
English |
Credits |
3 ECTS, Elective Course in Machine Learning Module II (computer science M.Sc.) |
ISIS |
Topics
This course provides a series of lectures on probabilistic modeling and inference, covering the following topics:
- Bayesian learning
- Gaussian process and Bayesian optimization
- Variational inference
- Generative modeling
- Bayesian deep learning
- Sampling methods
Enrollment / Limited number of participants
If you intend to participate, please send an e-mail to nakajima@tu-berlin.de with the title "Probabilistic Modeling and Inference Enrollment," and the main text including
- Full name
- Matr. no.
- Affiliation (University, Department, master/bachelor)
- Email address
- Module name (CA, ML, or standalone (only for non-TU students))
Participation spots are assigned on a first come/first serve basis.
Students from other universities
If you are not a student at TU and want to earn credit, you have to solicit ''Nebenhörerschaft'':
First fill the online ''form'' with the following details on the final page.
Course title and type
Program number
Number of course hours per week (SWS)
Lecturer
Probabilistic Modeling and Inference
0434 L 503
2
Shinichi Nakajima
- Send the form to one of the trainers (see contact information above) to sign
- Then send the signed form to Manuela Gadow (manuela.gadow at tu-berlin.de), who is authorized to sign on behalf of our dean.
After getting your signed form back send it together with your current matriculation letter ('Immatrikulationsbescheinigung') to the student registration office (nebenhoerer@studsek.tu-berlin.de).