International Center for Computational Logic - Benutzerbeiträge [de]

Dagmar Gromann

2019-02-26T13:21:36Z

Dgromann:

{{Mitarbeiter
|Vorname=Dagmar
|Nachname=Gromann
|Akademischer Titel=Dr.
|Forschungsgruppe=Computational Logic
|Stellung=Wissenschaftliche Mitarbeiterin
|Ehemaliger=0
|Telefon=+49 351 463 43560
|Fax=+49 351 463 32827
|Email=dagmar_gromann@tu-dresden.de
|Raum=APB 2036
|Bild=DagmarFoto.png
|Info=I joined the [[Computational Logic/en|Computational Logic Group]] at the Technical University Dresden as post-doc researcher in November 2017. Prior to that I had been working with [http://www.iiia.csic.es/~marco/Bio.html Marco Schorlemmer] at the [https://www.iiia.csic.es/ Artificial Intelligence Research Institute (IIIA)] in Barcelona as a post-doc researcher within the [https://www.essence-network.com ESSENCE Marie Curie Initial Training Network] from November 2015 to October 2017, a project that focused on enabling computational systems to share, negotiate, and evolve meaning in ways similar to human communication without prior agreement on shared semantics. Until October 2015, I was a prae-doc research assistant at the [https://www.wu.ac.at/ Vienna University of Economics and Business] being supervised by [https://ucris.univie.ac.at/portal/de/persons/gerhard-budin(ece64bdb-6052-4807-b8bd-e8bd0373fedc).html Prof. Gerhard Budin]. My original background is in linguistics and computer science. After working in industry for several years for companies such as Siemens, Roche Diagnostics, and Berlitz, I decided to return to academia.

My research interests include:
*[[Semantische Technologien |Semantic Web Technologies]]
*[[Wissensrepräsentation und logisches Schließen/en |Knowledge Representation]]
*Natural Language Processing (NLP) and Understanding
|Info EN=I joined the [[Computational Logic/en|Computational Logic Group]] at the Technical University Dresden as post-doc researcher in November 2017. Prior to that I had been working with [http://www.iiia.csic.es/~marco/Bio.html Marco Schorlemmer] at the [https://www.iiia.csic.es/ Artificial Intelligence Research Institute (IIIA)] in Barcelona as a post-doc researcher within the [https://www.essence-network.com ESSENCE Marie Curie Initial Training Network] from November 2015 to October 2017, a project that focused on enabling computational systems to share, negotiate, and evolve meaning in ways similar to human communication without prior agreement on shared semantics. Until October 2015, I was a prae-doc research assistant at the [https://www.wu.ac.at/ Vienna University of Economics and Business] being supervised by [https://ucris.univie.ac.at/portal/de/persons/gerhard-budin(ece64bdb-6052-4807-b8bd-e8bd0373fedc).html Prof. Gerhard Budin]. My original background is in linguistics and computer science. After working in industry for several years for companies such as Siemens, Roche Diagnostics, and Berlitz, I decided to return to academia.

My research interests include:
*[[Semantische Technologien |Semantic Web Technologies]]
*[[Wissensrepräsentation und logisches Schließen/en |Knowledge Representation]]
*Natural Language Processing (NLP) and Understanding
|DBLP=http://dblp.uni-trier.de/pers/hd/g/Gromann:Dagmar
|Google Scholar=https://scholar.google.de/citations?user=0GoPTqsAAAAJ&hl=de&oi=ao
|Alternative URI=http://dagmargromann.com/
|Publikationen anzeigen=1
|Abschlussarbeiten anzeigen=1
}}
{{Forschungsgebiet Auswahl
|Forschungsgebiet=Wissensrepräsentation und logisches Schließen
}}
{{Forschungsgebiet Auswahl
|Forschungsgebiet=Semantische Technologien
}}

Semantic Computing (WS 2018/2019) (WS2018)

2019-01-27T15:46:38Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065
* Monday, 14.1.2019, 11.10 --12.40 am (lecture), Room APB/E007
* Monday, 14.1.2019, 1.00 -- 2.30 pm (tutorial, Room APB/E0069

Lecture free periods are:
* Friday, 12 October 2018
* Friday, 14 December 2018 (instead: Monday 14.1.2019 11.10 -- 2.30 pm; see above)
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2036), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,Lecture-07.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 8
|Room=APB E005
|Date=2018/12/06
|DS=DS1
|Download=Lecture-08.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 9
|Room=APB E005
|Date=2018/12/20
|DS=DS1
|Download=Lecture-09.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 10
|Room=APB E005
|Date=2019/01/11
|DS=DS1
|Download=Lecture-10.pdf,Lecture-10.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 11
|Room=APB E005
|Date=2019/01/14
|DS=DS1
|Download=Lecture-11.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 12
|Room=APB E005
|Date=2019/01/18
|DS=DS1
|Download=Lecture-12.pdf,Lecture-12.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture Ontology Learning (for your reference only - not part of exam)
|Room=APB E005
|Date=2019/01/25
|DS=DS1
|Download=Lecture-13.pdf,
}}

Semantic Computing (WS 2018/2019) (WS2018)

2019-01-25T10:12:40Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065
* Monday, 14.1.2019, 11.10 --12.40 am (lecture), Room APB/E007
* Monday, 14.1.2019, 1.00 -- 2.30 pm (tutorial, Room APB/E0069

Lecture free periods are:
* Friday, 12 October 2018
* Friday, 14 December 2018 (instead: Monday 14.1.2019 11.10 -- 2.30 pm; see above)
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2036), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,Lecture-07.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 8
|Room=APB E005
|Date=2018/12/06
|DS=DS1
|Download=Lecture-08.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 9
|Room=APB E005
|Date=2018/12/20
|DS=DS1
|Download=Lecture-09.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 10
|Room=APB E005
|Date=2019/01/11
|DS=DS1
|Download=Lecture-10.pdf,Lecture-10.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 11
|Room=APB E005
|Date=2019/01/14
|DS=DS1
|Download=Lecture-11.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 12
|Room=APB E005
|Date=2019/01/18
|DS=DS1
|Download=Lecture-12.pdf,Lecture-12.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 13
|Room=APB E005
|Date=2019/01/25
|DS=DS1
|Download=Lecture-13.pdf,
}}

Datei:Lecture-12.pdf

2019-01-25T10:12:36Z

Dgromann: Dgromann lud eine neue Version von „Datei:Lecture-12.pdf“ hoch

Datei:Lecture-13.pdf

2019-01-25T07:34:51Z

Dgromann: Dgromann lud eine neue Version von „Datei:Lecture-13.pdf“ hoch

Semantic Computing (WS 2018/2019) (WS2018)

2019-01-23T14:25:34Z

2019-01-18T08:16:41Z

2019-01-04T12:34:47Z

Dgromann:

{{Mitarbeiter
|Vorname=Dagmar
|Nachname=Gromann
|Akademischer Titel=Dr.
|Forschungsgruppe=Computational Logic
|Stellung=Wissenschaftliche Mitarbeiterin
|Ehemaliger=0
|Telefon=+49 351 463 38679
|Fax=+49 351 463 32827
|Email=dagmar_gromann@tu-dresden.de
|Raum=APB 2036
|Bild=DagmarFoto.png
|Info=I joined the [[Computational Logic/en|Computational Logic Group]] at the Technical University Dresden as post-doc researcher in November 2017. Prior to that I had been working with [http://www.iiia.csic.es/~marco/Bio.html Marco Schorlemmer] at the [https://www.iiia.csic.es/ Artificial Intelligence Research Institute (IIIA)] in Barcelona as a post-doc researcher within the [https://www.essence-network.com ESSENCE Marie Curie Initial Training Network] from November 2015 to October 2017, a project that focused on enabling computational systems to share, negotiate, and evolve meaning in ways similar to human communication without prior agreement on shared semantics. Until October 2015, I was a prae-doc research assistant at the [https://www.wu.ac.at/ Vienna University of Economics and Business] being supervised by [https://ucris.univie.ac.at/portal/de/persons/gerhard-budin(ece64bdb-6052-4807-b8bd-e8bd0373fedc).html Prof. Gerhard Budin]. My original background is in linguistics and computer science. After working in industry for several years for companies such as Siemens, Roche Diagnostics, and Berlitz, I decided to return to academia.

My research interests include:
*[[Semantische Technologien |Semantic Web Technologies]]
*[[Wissensrepräsentation und logisches Schließen/en |Knowledge Representation]]
*Natural Language Processing (NLP) and Understanding
|Info EN=I joined the [[Computational Logic/en|Computational Logic Group]] at the Technical University Dresden as post-doc researcher in November 2017. Prior to that I had been working with [http://www.iiia.csic.es/~marco/Bio.html Marco Schorlemmer] at the [https://www.iiia.csic.es/ Artificial Intelligence Research Institute (IIIA)] in Barcelona as a post-doc researcher within the [https://www.essence-network.com ESSENCE Marie Curie Initial Training Network] from November 2015 to October 2017, a project that focused on enabling computational systems to share, negotiate, and evolve meaning in ways similar to human communication without prior agreement on shared semantics. Until October 2015, I was a prae-doc research assistant at the [https://www.wu.ac.at/ Vienna University of Economics and Business] being supervised by [https://ucris.univie.ac.at/portal/de/persons/gerhard-budin(ece64bdb-6052-4807-b8bd-e8bd0373fedc).html Prof. Gerhard Budin]. My original background is in linguistics and computer science. After working in industry for several years for companies such as Siemens, Roche Diagnostics, and Berlitz, I decided to return to academia.

My research interests include:
*[[Semantische Technologien |Semantic Web Technologies]]
*[[Wissensrepräsentation und logisches Schließen/en |Knowledge Representation]]
*Natural Language Processing (NLP) and Understanding
|DBLP=http://dblp.uni-trier.de/pers/hd/g/Gromann:Dagmar
|Google Scholar=https://scholar.google.de/citations?user=0GoPTqsAAAAJ&hl=de&oi=ao
|Alternative URI=http://dagmargromann.com/
|Publikationen anzeigen=1
|Abschlussarbeiten anzeigen=1
}}
{{Forschungsgebiet Auswahl
|Forschungsgebiet=Wissensrepräsentation und logisches Schließen
}}
{{Forschungsgebiet Auswahl
|Forschungsgebiet=Semantische Technologien
}}

Semantic Computing (WS 2018/2019) (WS2018)

2019-01-04T12:34:30Z

Dgromann:

2018-12-06T16:07:00Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065
* Monday, 14.1.2019, 11.10 --12.40 am (lecture), Room APB/E007
* Monday, 14.1.2019, 1.00 -- 2.30 pm (tutorial, Room APB/E0069

Lecture free periods are:
* Friday, 12 October 2018
* Friday, 14 December 2018 (instead: Monday 14.1.2019 11.10 -- 2.30 pm; see above)
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2034), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,Lecture-07.pdf,
}}

Semantic Computing (WS 2018/2019) (WS2018)

2018-12-06T15:46:12Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065
* Monday, 14.1.2019, 11.10 --12.40 am (lecture), Room APB/E007

Lecture free periods are:
* Friday, 12 October 2018
* Friday, 14 December 2018 (instead: Monday, 14.1.2019, 11:10 -- 12.40 am, Room APB/E007)
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2034), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,Lecture-07.pdf,
}}

Semantic Computing (WS 2018/2019) (WS2018)

2018-12-04T17:44:31Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065

Lecture free periods are:
* Friday, 12 October 2018
* Friday, 14 December 2018 (will be made up in January)
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2034), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,Lecture-07.pdf,
}}

Datei:Lecture-07.pdf

2018-12-04T17:44:29Z

Dgromann: Dgromann lud eine neue Version von „Datei:Lecture-07.pdf“ hoch

Semantic Computing (WS 2018/2019) (WS2018)

2018-12-04T10:53:45Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065

Lecture free periods are:
* Friday, 12 October 2018
* Friday, 14 December 2018 (will be made up in January)
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2034), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,
}}

Semantic Computing (WS 2018/2019) (WS2018)

2018-11-30T07:00:10Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065

Lecture free periods are:
* Friday, 12 October 2018
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2034), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,
}}

Datei:Lecture-06.pdf

2018-11-30T07:00:07Z

Dgromann: Dgromann lud eine neue Version von „Datei:Lecture-06.pdf“ hoch

Semantic Computing (WS 2018/2019) (WS2018)

2018-11-29T23:08:30Z

Dgromann:

{{Vorlesung
|Title=Semantic Computing
|Research group=Computational Logic
|Lecturers=Dagmar Gromann
|Tutors=Dagmar Gromann;
|Term=WS
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSExercise=2
|SWSPractical=0
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

==Schedule==
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
* Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
* Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065

Lecture free periods are:
* Friday, 12 October 2018
* Saturday, 22 December 2018 to Sunday 06 January 2019

==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 Semantic Computing GitHub].

====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 THIS LINK], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2034), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

==Resources==
* [https://wordnet.princeton.edu/ WordNet]
* [http://www.sfs.uni-tuebingen.de/GermaNet/ GermanNet]
* [http://compling.hss.ntu.edu.sg/omw/ Multilingual WordNet]

==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf online edition]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org online edition]

==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf online edition ]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

==Computational Linguistics==
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html online 2nd edition], [https://web.stanford.edu/~jurafsky/slp3/ partial 3rd edition]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book online edition]
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html online edition]

==Finding Conference Contributions==
* In general: [http://scholar.google.de Google Scholar]
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ ACL Anthology]
* Deep learning: [http://www.arxiv-sanity.com/ Arxiv Sanity]
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,
}}

Datei:Lecture-07.pdf

2018-11-29T23:06:59Z

Dgromann: Dgromann lud eine neue Version von „Datei:Lecture-07.pdf“ hoch