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| Machine Learning 2 is a 9 LP (9 ECTS) credits module. Machine Learning 2-X is a 12 LP (12 ECTS) credits module. |
* Machine Learning 2 is a 9 LP (9 ECTS) credits module. * Machine Learning 2-X is a 12 LP (12 ECTS) credits module. '''Note: Due to the current situation, the course will take place online.''' |
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| ||'''Lecture'''|| Thursdays 14:00-16:00 || ||'''Exercise session'''|| Thursdays 16:00-18:00 || |
||'''Lecture'''|| Mondays 14:00-16:00 || ||'''Exercise session'''|| Mondays 16:00-18:00 || |
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| || '''ISIS''' || TBA || | || '''ISIS''' || https://isis.tu-berlin.de/course/view.php?id=19491 || |
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| '''How many credits for this course?''' 9 ECTS if taken as a standalone course. 12 ECTS if taking the whole module (i.e. including an optional course, see below for a list). |
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| '''I am from a different university, can I take this course?''' If you are not a student at TU and want to earn credit, you have to solicit [[http://www.tu-berlin.de/?id=76326|''Nebenhörerschaft'']]: (1) Print out the forms concerning Nebenhörerschaft you find on that page. (2) Pass by at my office (see above) to have them signed, or bring your forms during the first lecture. (3) In addition, the dean of faculty IV has to sign. (4) Register at the the Campus Center. You will receive a TUBIT account (see below). | '''I am from a different university, can I take this course?''' If you are not a student at TU and want to earn credit, you have to solicit [[http://www.tu-berlin.de/?id=76326|''Nebenhörerschaft'']]: (1) Print out the forms concerning Nebenhörerschaft you find on that page. (2) Pass by at my office (see above) to have them signed, or bring your forms during the first lecture. (3) In addition, the dean of faculty IV has to sign. (4) Register at the the Campus Center. You will receive a TUBIT account (see below). '''Note: Due to the current situation, it is not possible to register to the course as a Nebenhörer'''. |
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| * Embeddings * Stationary Subspace Analysis * Independent Component Analysis * Canonical Correlation Analysis * Kernel methods for structured data * Deep learning methods for structured data * Explainable machine learning * Structured output learning * Anomaly detection * Bioinformatics |
* Embeddings (LLE, TSNE) * Component Analyses (CCA, ICA) * Kernel Learning (structured input, structured outputs, bioinformatics, anomaly detection) * Deep Learning (convolutional neural networks, generative adversarial networks, XAI) * Federated Learning |
Machine Learning 2(-X)
General Information
- Machine Learning 2 is a 9 LP (9 ECTS) credits module.
- Machine Learning 2-X is a 12 LP (12 ECTS) credits module.
Note: Due to the current situation, the course will take place online.
Lecture period |
20.04.2020-18.07.2020 |
Lecture |
Mondays 14:00-16:00 |
Exercise session |
Mondays 16:00-18:00 |
Language |
English |
Trainers |
Wojciech Samek |
Gregoire Montavon |
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Contact |
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ISIS |
Frequently asked questions (FAQ):
Is it possible to take this course without having taken ML1? Yes, ML1 is not a formal prerequisite. However, methods learned in ML1 will be assumed to be known, and extra work might therefore be needed during the first few weeks.
I am from a different university, can I take this course? If you are not a student at TU and want to earn credit, you have to solicit ''Nebenhörerschaft'': (1) Print out the forms concerning Nebenhörerschaft you find on that page. (2) Pass by at my office (see above) to have them signed, or bring your forms during the first lecture. (3) In addition, the dean of faculty IV has to sign. (4) Register at the the Campus Center. You will receive a TUBIT account (see below). Note: Due to the current situation, it is not possible to register to the course as a Nebenhörer.
Topics
- Embeddings (LLE, TSNE)
- Component Analyses (CCA, ICA)
- Kernel Learning (structured input, structured outputs, bioinformatics, anomaly detection)
- Deep Learning (convolutional neural networks, generative adversarial networks, XAI)
- Federated Learning