Description:
In this years course, we explore how to extract valuable insights from unstructured text data using machine learning methods. Throughout this course, you'll delve into fundamental concepts like Bag of Words and Word Embeddings, enabling you to transform raw text into structured representations.
We will also delve into the realm of Machine Learning Methods, mastering both supervised and unsupervised algorithms to make informed predictions and classifications. From sentiment analysis to topic modeling, these techniques offer powerful tools for deciphering the underlying patterns in textual data.
But this course isn't just about theory. It's about practical application. You'll learn how to integrate Text as Data models and Machine Learning Methods into your research, empowering you to address questions in your field. In the end of this course, you will have the chance to write and to present a term paper of your choice.
Time schedule:
Week 1 to 5: Lecture on Theory
Week 6 to 10: Tutorial Q&A on how to apply methods for your term paper
Week 11 to 15: Presentation of term paper
Literature:
Natural Language Processing with Python by Steven Bird,
Machine Learning with Python by Andreas C. Müller und Sarah Guido
Time & venue:
Tuesdays, 8:30-10:00; University of Potsdam, Campus Griebnitzsee, Room 3.06. S22
Exam:
Term Paper and Presentation
Maximilian Andres