Natural Language Processing

Wednesday 4 - 5:45 pm // The course will provide you with knowledge and experience about most recent and advanced machine learning models for natural language processing.


Für diesen Kurs kann man keine Teilnahmenbescheinigung erhalten.

Bei ausreichender Teilnahme und Erbringung eines der angebotenen Leistungsnachweise kannst Du ein Leistungszertifikat erwerben.



This course is a hybrid course; you can participate either online via Zoom or in presence in Kiel (the number of places for presence participation is very limited).

Our courses are open to all and free of charge for students, pupils, trainees, founders, scientific staff, job seekers, refugees, and pensioners. For employees and self-employed persons, a fee of 200 euros is charged when acquiring a certificate of achievement. Employees and self-employed persons can apply for a scholarship via

Your Background

You already have some knowledge about Machine Learning (ideally you followed one of our previous courses) and are interested in learning more about Natural Language Processing. Particularly in this course you should be dmotivate to bring your own contribution – it's a great opportunity to learn and discuss latest discoveries in the field.

Course Content

This course is based on the Coursera Specialization on Natural Language Processing (

The course will provide you with knowledge and experience about most recent and advanced machine learning models for natural language processing. You will be able to train and use neural network to provide auto-correction suggestion, complete sentences and even build a chatbot.
You will then apply the newly acquired knowledge to implement your own machine learning project in a team.

How it works

The course will take place every Wednesdays from 4 pm to 5:45 pm.

Before the first meeting there will be a semesterkick-off on the 05.11.2020.

During the week you will be expected to work through the assigned online course content, which will take you between 4 to 5 hours each week, open questions considering the course content and possible additional implications will then be discussed in the weekly offline course on Monday. Towards the end of the semester you will then work in a team on your own machine learning project.
All needed software and online course content is free. For the practical assignments in the online course, however, it will be necessary to create a Google account.

The Formalities

In order to receive a certificate of attendance ("Leistungszertifikat") for this course, active participation is expected, and no more than two classes may be missed. The active participation is proven via the final presentation of your project by you and your team, and the delivery of a well documented project source code. The same conditions apply in order to receive ECTS.

In the online sessions it is necessary that you always provide your full name in Zoom so that your presence is registered on the EDU platform. No mere certificate of attendance will be issued for this course.

Further details may be given in the course.


Natural Language Processing
Wintersemester 2020/2021
16:00 - 17:45 Uhr


Henrik Horst
Start: 05.11.2020
Ende: 27.01.2021
General introduction about the course, the content and the projects. A round of introduction from the participants.

Luca Palmieri

Learn to extract features from text into numerical vectors, then build a binary classifier for tweets using a logistic regression!

Luca Palmieri

Learn the theory behind Bayes' rule for conditional probabilities, then apply it toward building a Naive Bayes tweet classifier of your own!

Luca Palmieri

Vector space models capture semantic meaning and relationships between words. You'll learn how to create word vectors that capture dependencies between words, then visualize their relationships in two dimensions using PCA.

Luca Palmieri

Learn to transform word vectors and assign them to subsets using locality sensitive hashing, in order to perform machine translation and document search.

Luca Palmieri

Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!

Luca Palmieri

Learn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus!

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Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter!

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Learn about how word embeddings carry the semantic meaning of words, which makes them much more powerful for NLP tasks, then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.

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