136040 VU Practical Machine Learning for Natural Language Processing (2023S)
Continuous assessment of course work
Labels
MIXED
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 06.02.2023 08:00 to Mo 27.02.2023 08:00
- Deregistration possible until Fr 31.03.2023 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 02.03. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 07.03. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 09.03. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 14.03. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 16.03. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 21.03. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 23.03. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 28.03. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 30.03. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 18.04. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 20.04. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 25.04. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 27.04. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 02.05. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 04.05. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 09.05. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 11.05. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 16.05. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 23.05. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 25.05. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 01.06. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 06.06. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 13.06. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 15.06. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 20.06. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 22.06. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Tuesday 27.06. 09:45 - 11:15 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
- Thursday 29.06. 11:30 - 13:00 Seminarraum 6 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Information
Aims, contents and method of the course
In this lecture, basic machine learning algorithms are implemented in Python and applied to natural language processing problems. The focus is on vector representations of texts, and the methods range from text classification using the perceptron algorithm to word vectors and simple neural networks. Basic knowledge of Python or the willingness to acquire it quickly is assumed (the basic control and data structures, such as class definitions or dictionaries). The language of the lecture is German or English (depending on the lecturer).
Assessment and permitted materials
There will be regular assignments during the semester and a written exam at the end.
Minimum requirements and assessment criteria
Regularly working on assignments during the semester and achieving a minimum number of points in an exam.
Examination topics
Knowledge of the algorithms and machine learning methods discussed in the lecture, as well as their application and implementation discussed in the exercise.
Reading list
“Marc Pilgrim: Dive into Python”
https://diveintopython3.problemsolving.io/“Hal Daume: A course in machine learning” Kapitel 4,5,7,10
http://ciml.info/“Goldberg & Levy: word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method”
https://arxiv.org/abs/1402.3722“Christopher Olah’s blog”
http://colah.github.io/“Goodfellow et al.: Deep Learning” - (advanced)
https://www.deeplearningbook.org/“Keras Developer Guides”
https://keras.io/guides/
https://diveintopython3.problemsolving.io/“Hal Daume: A course in machine learning” Kapitel 4,5,7,10
http://ciml.info/“Goldberg & Levy: word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method”
https://arxiv.org/abs/1402.3722“Christopher Olah’s blog”
http://colah.github.io/“Goodfellow et al.: Deep Learning” - (advanced)
https://www.deeplearningbook.org/“Keras Developer Guides”
https://keras.io/guides/
Association in the course directory
S-DH (Cluster I: Language and Literature)
Last modified: Th 04.07.2024 00:13