International Center for Computational Logic - Benutzerbeiträge [de]

Sylvia Wünsch

2023-03-13T05:44:46Z

Romy Thieme:

{{Mitarbeiter
|Vorname=Sylvia
|Nachname=Wünsch
|Stellung=Sekretärin
|Ehemaliger=0
|Telefon=+49 351 463 38341
|Fax=+49 351 463 38342
|Email=sylvia.wuensch@tu-dresden.de
|Raum=APB 2006
|Publikationen anzeigen=0
|Abschlussarbeiten anzeigen=0
}}

Wissensverarbeitung

2023-01-17T10:35:34Z

Romy Thieme:

{{Forschungsgruppe
|Name EN=Knowledge Representation and Reasoning
|Kurzname=KRR
|Beschreibung DE=Die Professur Wissensverarbeitung untergliedert sich gegenwärtig in zwei Schwerpunkte: menschliches Schließen in Logikprogramme abzubilden, und das Erfüllbarkeitsproblem und damit verwandte Probleme maschinell zu lösen. Auf beiden Gebieten wird sowohl aktiv geforscht, als auch Lehre mit aktuellsten Inhalten gehalten.
|Beschreibung EN=The Knowledge Representation and Reasoning group has two major parts: human reasoning and solving the satisfiability testing and related decision and discrete optimization problems. In both areas we focus on research, and on the other hand teach with most recent research results.
|Forschungsgruppenleiter=Steffen Hölldobler
|Bild=Kiwv-group-picture.jpg
|Ehemalige Forschungsgruppe=1
}}

Wissensverarbeitung

2023-01-17T10:34:53Z

Romy Thieme:

{{Forschungsgruppe
|Name EN=Knowledge Representation and Reasoning
|Kurzname=KRR
|Beschreibung DE=Die Professur Wissensverarbeitung untergliedert sich gegenwärtig in zwei Schwerpunkte: menschliches Schließen in Logikprogramme abzubilden, und das Erfüllbarkeitsproblem und damit verwandte Probleme maschinell zu lösen. Auf beiden Gebieten wird sowohl aktiv geforscht, als auch Lehre mit aktuellsten Inhalten gehalten.
|Beschreibung EN=The Knowledge Representation and Reasoning group has two major parts: human reasoning and solving the satisfiability testing and related decision and discrete optimization problems. In both areas we focus on research, and on the other hand teach with most recent research results.

Currently, we have no access to university phones. Please contact us via E-Mail. Offices are closed from public access. For urgent matters, you can contact Steffen Hölldobler at 0151 27023623.
|Forschungsgruppenleiter=Steffen Hölldobler
|Bild=Kiwv-group-picture.jpg
|Ehemalige Forschungsgruppe=1
}}

Foundations for Machine Learning (SS2019)

2019-07-04T10:33:12Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/0/0a/ML2019tud_04p.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/5/52/ML2019tud_05.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/a/a5/ML2019tud_06.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/e/e2/ML2019tud_07.pdf Slide 7]
*[https://iccl.inf.tu-dresden.de/w/images/9/9a/ML2019tud_08.pdf Slide 8]
*[https://iccl.inf.tu-dresden.de/w/images/9/9b/ML2019tud_09.pdf Slide 9]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 09.pdf

2019-07-04T10:32:25Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-07-04T10:31:27Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/0/0a/ML2019tud_04p.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/5/52/ML2019tud_05.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/a/a5/ML2019tud_06.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/e/e2/ML2019tud_07.pdf Slide 7]
*[https://iccl.inf.tu-dresden.de/w/images/9/9a/ML2019tud_08.pdf Slide 8]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 08.pdf

2019-07-04T10:30:51Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-07-04T10:30:12Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/0/0a/ML2019tud_04p.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/5/52/ML2019tud_05.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/a/a5/ML2019tud_06.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/e/e2/ML2019tud_07.pdf Slide 7]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 07.pdf

2019-07-04T10:29:28Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-07-02T07:12:24Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/0/0a/ML2019tud_04p.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/5/52/ML2019tud_05.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/a/a5/ML2019tud_06.pdf Slide 6]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 06.pdf

2019-07-02T07:11:37Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-07-02T07:11:09Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/0/0a/ML2019tud_04p.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/5/52/ML2019tud_05.pdf Slide 5]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 05.pdf

2019-07-02T07:10:25Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-06-26T06:47:04Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/0/0a/ML2019tud_04p.pdf Slide 4]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 04p.pdf

2019-06-26T06:46:12Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-06-24T10:58:29Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/b/bc/ML2019tud_03p.pdf Slide 3]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 03p.pdf

2019-06-24T10:57:27Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-06-19T11:24:44Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/3/37/ML2019tud_02.pdf Slide 2]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML2019tud 02.pdf

2019-06-19T11:23:46Z

Romy Thieme:

Foundations for Machine Learning (SS2019)

2019-06-18T09:54:05Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/7/79/ML2019tud_01-1.pdf Slide 1]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Foundations for Machine Learning (SS2019)

2019-06-18T09:26:43Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2, CMS-VC-ELV1, CMS-VC-ELV2, CMS-CLS-ELG
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===
 Please check this website regularly as there might be changes! 

The lecture will take place from 17th June till 5th July 2019 on the following days:

* Monday, 17th June from 14:50 - 16:20 pm at APB 2026 (room might change depending on number of students)
* Tuesday, 18th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Friday, 21st June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 24th June from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 25th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 26th June from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 27th June from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 28th June from 09:20 - 10:50 am at APB E046 (Andreas-Pfitzmann-Bau)

* Monday, 1st July from 14:50 - 16:20 pm at SCH/A215/H (Schumann-Bau)
* Tuesday, 2nd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Wednesday, 3rd July from 14:50 - 16:20 pm at SCH/A216/H (Schumann-Bau)
* Thursday, 4th July from 16:40 - 18:10 pm at HSZ/105/U (Hörsaalzentrum)
* Friday, 5th July from 09:20 - 10:50 am at HSZ/101/U (Hörsaalzentrum)

The written examination will be on 8th July, 2019 from 14:50 - 16:20 pm at HÜL/S186/H (Hülsse-Bau)

===Registration===

'''Registration for the course by 30th May, 2019'''

'''* CL-Students: please send an Email to cl@mailbox.tu-dresden.de'''

'''* CMS-Students: Please register via SELMA-portal and consider the valid deadline'''

'''* Other Students: Please register via jexam'''
https://jexam.inf.tu-dresden.de/de.jexam.web.v4.5/spring/welcome

===Lecture Slides===

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Foundations for Machine Learning (SS2019)

2019-06-18T09:24:36Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2019
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

* Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)

===Lecture Slides===

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Foundations for Machine Learning (SS2019)/en

2019-06-18T09:23:55Z

Romy Thieme: Page created automatically by parser function on page Foundations for Machine Learning (SS2019)

Foundations for Machine Learning (SS2019)

2019-06-18T09:23:54Z

Romy Thieme: Die Seite wurde neu angelegt: „{{Vorlesung |Title=Foundations for Machine Learning |Research group=Wissensverarbeitung |Lecturers=Yohanes Stefanus |Term=SS |Year=2018 |Module=MCL-AI, MCL-PI,…“

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2018
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

* Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)

===Lecture Slides===

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Foundations for Machine Learning(SS2019)/en

2019-06-18T09:21:10Z

Romy Thieme: Page created automatically by parser function on page Foundations for Machine Learning(SS2019)

Datei:ML2019tud 01-1.pdf

2019-06-18T05:44:39Z

Romy Thieme:

Fuzzy Informationsverarbeitung (SS2019)

2019-05-17T08:44:11Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

'''Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:'''

* Montag: 4. DS (13:00 - 14:30 Uhr)
* Dienstag: 4. DS (13:00 - 14:30 Uhr)
* Mittwoch: 4. DS (13:00 - 14:30 Uhr)
* Donnerstag: 2. DS (09:20 - 10:50 Uhr)
* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T11:57:50Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=3
|SWSExercise=0
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

'''Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:'''

* Montag: 4. DS (13:00 - 14:30 Uhr)
* Dienstag: 4. DS (13:00 - 14:30 Uhr)
* Mittwoch: 4. DS (13:00 - 14:30 Uhr)
* Donnerstag: 2. DS (09:20 - 10:50 Uhr)
* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T10:33:33Z

Romy Thieme:

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:41:38Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=0
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

'''Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:'''

* Montag: 4. DS (13:00 - 14:30 Uhr)

* Dienstag: 4. DS (13:00 - 14:30 Uhr)

* Mittwoch: 4. DS (13:00 - 14:30 Uhr)

* Donnerstag: 2. DS (09:20 - 10:50 Uhr)

* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:35:03Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

'''Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:'''

* Montag: 4. DS (13:00 - 14:30 Uhr)

* Dienstag: 4. DS (13:00 - 14:30 Uhr)

* Mittwoch: 4. DS (13:00 - 14:30 Uhr)

* Donnerstag: 2. DS (09:20 - 10:50 Uhr)

* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:25:51Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:

* Montag: 4. DS (13:00 - 14:30 Uhr)

* Dienstag: 4. DS (13:00 - 14:30 Uhr)

* Mittwoch: 4. DS (13:00 - 14:30 Uhr)

* Donnerstag: 2. DS (09:20 - 10:50 Uhr)

* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:25:07Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:

* Montag: 4. DS (13:00 - 14:30 Uhr)

* Dienstag: 4. DS (13:00 - 14:30 Uhr)

* Mittwoch: 4. DS (13:00 - 14:30 Uhr)

* Donnerstag: 2. DS (09:20 - 10:50 Uhr)

* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:24:19Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungstermine===

Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:

* Montag: 4. DS (13:00 - 14:30 Uhr)
* Dienstag: 4. DS (13:00 - 14:30 Uhr)
* Mittwoch: 4. DS (13:00 - 14:30 Uhr)
* Donnerstag: 2. DS (09:20 - 10:50 Uhr)
* Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:23:20Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description====Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungen===

Die Vorlesungen finden vom 24.06.2019 - 05.07.2019 jeweils an folgenden Tagen im Raum APB/2026 statt:

Montag: 4. DS (13:00 - 14:30 Uhr)
Dienstag: 4. DS (13:00 - 14:30 Uhr)
Mittwoch: 4. DS (13:00 - 14:30 Uhr)
Donnerstag: 2. DS (09:20 - 10:50 Uhr)
Freitag: 4. DS (13:00 - 14:30 Uhr)
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T09:14:57Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description=

===Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungen===

}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T08:59:39Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description=

===Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

}}

===Vorlesungen===

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T08:59:02Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description=

===Inhalte===

In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungen===

}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T08:57:50Z

Romy Thieme:

{{Vorlesung
|Title=Fuzzy Informationsverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Khang Tran Dinh
|Term=SS
|Year=2019
|Module=INF-BAS2, INF-VERT2, INF-PM-FOR
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=Klausur, mündliche Prüfung
|Description=In dieser Vorlesung werden die Grundlagen der Darstellung und Verarbeitung von unscharfen Informationen vermittelt. Diese haben solche Aspekte wie Ungenauigkeit, Unbestimmtheit, Unsicherheit, usw. Die Herangehensweise besteht aus Fuzzy Mengen und Fuzzy Inferenz.

===Inhalte===

Der Kurs gliedert sich in die folgenden Inhalte:

- Linguistische Variablen,

- Fuzzy Mengen und Operationen von fuzzy Mengen,

- Fuzzy Relationen,

- Fuzzy Logik und fuzzy Inferenz,

- Fuzzy Systeme und Anwendungen,

- Typ-2-Fuzzy-Mengen und hedge-algebraische typ-2-Fuzzy-Mengen

===Vorlesungen===
}}

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T08:52:13Z

Romy Thieme:

Fuzzy Informationsverarbeitung (SS2019)/en

2019-05-16T08:44:17Z

Romy Thieme: Page created automatically by parser function on page Fuzzy Informationsverarbeitung (SS2019)

Fuzzy Informationsverarbeitung (SS2019)

2019-05-16T08:44:17Z

Foundations for Maschine Learning (SS2018)

2019-04-08T11:53:19Z

Romy Thieme:

Foundations for Maschine Learning (SS2019)/en

2019-04-04T10:42:36Z

Romy Thieme: Page created automatically by parser function on page Foundations for Maschine Learning (SS2019)

Foundations for Maschine Learning (SS2018)

2018-07-19T07:47:42Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2018
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

* Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/4/46/Ml-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/2/2d/Ml-2.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/8/81/Ml-3.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/b/b7/Ml-4.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/0/06/Ml-5.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/4/4c/Ml-6.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/b/b1/Ml-7.pdf Slide 7]
*[https://iccl.inf.tu-dresden.de/w/images/5/57/Ml-8.pdf Slide 8]
*[https://iccl.inf.tu-dresden.de/w/images/2/24/Ml-9.pdf Slide 9]
*[https://iccl.inf.tu-dresden.de/w/images/6/6a/Ml-10.pdf Slide 10]
*[https://iccl.inf.tu-dresden.de/w/images/4/4e/Ml-11.pdf Slide 11]
*[https://iccl.inf.tu-dresden.de/w/images/b/b5/ML-12.pdf Slide 12]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:ML-12.pdf

2018-07-19T07:46:22Z

Romy Thieme:

Foundations for Maschine Learning (SS2018)

2018-07-17T09:39:44Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2018
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

* Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/4/46/Ml-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/2/2d/Ml-2.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/8/81/Ml-3.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/b/b7/Ml-4.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/0/06/Ml-5.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/4/4c/Ml-6.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/b/b1/Ml-7.pdf Slide 7]
*[https://iccl.inf.tu-dresden.de/w/images/5/57/Ml-8.pdf Slide 8]
*[https://iccl.inf.tu-dresden.de/w/images/2/24/Ml-9.pdf Slide 9]
*[https://iccl.inf.tu-dresden.de/w/images/6/6a/Ml-10.pdf Slide 10]
*[https://iccl.inf.tu-dresden.de/w/images/4/4e/Ml-11.pdf Slide 11]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Datei:Ml-11.pdf

2018-07-17T09:38:41Z

Romy Thieme:

Foundations for Maschine Learning (SS2018)

2018-07-10T11:41:29Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2018
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|SWSLecture=2
|SWSExercise=1
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

* Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/4/46/Ml-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/2/2d/Ml-2.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/8/81/Ml-3.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/b/b7/Ml-4.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/0/06/Ml-5.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/4/4c/Ml-6.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/b/b1/Ml-7.pdf Slide 7]
*[https://iccl.inf.tu-dresden.de/w/images/5/57/Ml-8.pdf Slide 8]
*[https://iccl.inf.tu-dresden.de/w/images/2/24/Ml-9.pdf Slide 9]
*[https://iccl.inf.tu-dresden.de/w/images/6/6a/Ml-10.pdf Slide 10]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}

Foundations for Maschine Learning (SS2018)

2018-07-09T11:17:33Z

Romy Thieme:

{{Vorlesung
|Title=Foundations for Machine Learning
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus
|Term=SS
|Year=2018
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|SWSLecture=4
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description====Content===

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data.
Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc.
This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

===Schedule===

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

* Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)

===Lecture Slides===

*[https://iccl.inf.tu-dresden.de/w/images/4/46/Ml-1.pdf Slide 1]
*[https://iccl.inf.tu-dresden.de/w/images/2/2d/Ml-2.pdf Slide 2]
*[https://iccl.inf.tu-dresden.de/w/images/8/81/Ml-3.pdf Slide 3]
*[https://iccl.inf.tu-dresden.de/w/images/b/b7/Ml-4.pdf Slide 4]
*[https://iccl.inf.tu-dresden.de/w/images/0/06/Ml-5.pdf Slide 5]
*[https://iccl.inf.tu-dresden.de/w/images/4/4c/Ml-6.pdf Slide 6]
*[https://iccl.inf.tu-dresden.de/w/images/b/b1/Ml-7.pdf Slide 7]
*[https://iccl.inf.tu-dresden.de/w/images/5/57/Ml-8.pdf Slide 8]
*[https://iccl.inf.tu-dresden.de/w/images/2/24/Ml-9.pdf Slide 9]
*[https://iccl.inf.tu-dresden.de/w/images/6/6a/Ml-10.pdf Slide 10]

===Prerequisites===

* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0013333056/#detail '''here''']

* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
'''An electronic version of the book is accessable via TU network''' [https://katalogbeta.slub-dresden.de/id/0019910974/#detail '''here''']
}}