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+ + + + + + + + + + + + + + + + +This course aims to provide students with a foundational understanding of Natural Language Processing (NLP) concepts and techniques. +By the end of this course, students will be equipped to preprocess text data, engineer relevant features, and apply basic machine learning models on text data. +Additionally, students will gain insights into advanced NLP concepts like large language models and generative AI, along with their practical applications.
+The course is graded based on a written 90-minute exam at the end of the semester. +To be admitted to the exam, it is required to
+The presentation and assignments are ungraded.
+All course materials are provided in English. +Lectures will be held in German unless we have international students. +In this case, the lectures will also be in English.
+The course will be held in a hybrid format. +Approximately five lectures will be held in person. +The remaining lectures will be held online.
+ + + + + + + + + + + + + +The following skills are recommended to participate in the course.
+Basic programming skills
+To complete the course, you will need basic programming skills. +If you visited an introduction to programming course, you should be good to go. +We don't want to bother with advanced programming concepts but get excited with NLP! +So don't worry if you just started with programming.
+Basic Python skills
+The code for this lecture is written in Python, so it is definetely an advantage if you have worked with Python before. +However, if you are coming from a different langugage, you should be able to follow along. +I tried to keep the language specific parts to a minimum and will provide explanations where necessary.
+Microsoft provides a nice beginner Python course that you can take to get up to speed.
+Basic knowledge of the Linux command line
+Since the course is designed for a Linux development environment, it is recommended to have some basic skills with the Linux command line. +However, all required commands will be provided in the instructions, so it is not necessary to have Linux command line skills. +On Linux and Mac, the setup should work out of the box.
+If you are on Windows, it is recommended to use the Windows Subsystem for Linux (WSL). +Native Windows is not supported, but you should still be able to get everything running in an Anacoda environment.
+Basic knowledge of Git
+The course material is hosted on GitHub pages and you can access it through the browser. +As for the assignments, you will need to clone the repository and set up the development environment. +Also since the course is still in development, you will need to pull the latest changes from time to time.
+Lectures: 1
+The first part introduces NLP, with all important terminology for the course. It also covers some basic preprocessing aspects, as well as feature engineering.
+Lectures: 1,5
+This part will build the bridge from NLP basics to basic machine learning approaches. It will show how we can transform text into vectors and apply machine learning models on it. Students will learn or recap simple yet powerful machine learning concepts.
+Lectures: 1,5
+This part covers probabilistic NLP approaches, and familiarizes with the mathematical concepts behind them. This part should get a little more weight, as the statistical methods are interesting, still somewhat intuitive, and at the same time rather easy to implement. It will show the students that even with leight-weight approaches, we can solve impressing NLP tasks.
+Lectures: 3
+This part covers somewhat advanced topics, they should be only explained on a high level. It is more about transporting the idea of how SOTA NLP models work, without going much into architectural detail. Additionally, practical aspects should be covered, to get a feeling for real-life NLP scenarios.
+Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media. https://www.oreilly.com/library/view/natural-language-processing/9780596803346/ Also available open source: https://www.nltk.org/book_1ed/
+Jurafsky, D., & Martin, J. H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd ed.). Prentice Hall. https://www.amazon.de/dp/0131873210 Draft of 3rd edition also available open source: https://web.stanford.edu/~jurafsky/slp3/
+Tunstall, L., Von Werra, L., & Wolf, T. (2022). Natural Language Processing with Transformers. O'Reilly Media. https://www.oreilly.com/library/view/natural-language-processing/9781098136789/
+Vajjala, S., Majumder, B., Gupta, A., & Surana, H. (2020). Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. O'Reilly Media. https://www.oreilly.com/library/view/practical-natural-language/9781492054047/
+Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer. https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
+ + + + + + + + + + + + + +{"use strict";/*!
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