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| Semester | Herbstsemester 2024 |
| Angebotsmuster | unregelmässig |
| Dozierende | Benjamin Arold (benjamin.arold@unibas.ch, BeurteilerIn) |
| Inhalt | «Much of human knowledge is stored in unstructured formats, in particular in written text. This course teaches methods to process and analyze text data. The learning goals are to understand and implement text-as-data methods, and to evaluate the use of text-analysis tools in business and economics research. The course will conclude with an overview of non-standard data in business and economics beyond text, in particular audio data and image data. The course covers 10 topics, see below. For most topics, a theoretical lecture will be provided first, followed by a discussion of a recent research paper in economics/NLP, and concluded by a section of applied data analysis (in python), where the students are invited to code as well. The paper discussion will be conducted as a collaborative seminar, where students will take turns to present and discuss the papers. The 10 topics are ordered as follows: - Overview - Dictionaries - Tokenization & Distance - Unsupervised and Supervised ML with Text - Word Embeddings - Linguistic Parsing - Embedding Sequences with Attention - Extra: Using Transformers for YOUR Research - Audio Data in Business & Economics - Image Data in Business & Economics |
| Lernziele | The learning goals are to understand and implement text-as-data methods, and to evaluate the use of text-analysis tools in economics research. |
| Literatur | Books • Jurafsky and Martin, Speech and Language Processing (3d Ed. 2019). o Available here: https://web.stanford.edu/~jurafsky/slp3/ o The standard theory text on computational linguistics. • Natural Language Processing in Python, Third Edition (“NLTK Book”). o Available at nltk.org/book. o Classic treatments of traditional NLP tools. • Aurelien Geron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019) o O’Reilly Book, should be available with an academic account using ETH email. o A great practical book for machine learning and deep learning in Python, but not NLP-focused. We will use material from Chapters 2-4, 7-11, 13, and 15-17. o The deep learning chapters use Keras + TensorFlow. o Jupyter notebooks: https://github.com/ageron/handson-ml2 • Yoav Goldberg, Neural Network Methods for Natural Language Processing (2017) o ETH Library Online Access (email me if this doesn’t work) o A more advanced theoretical treatment of neural networks with an NLP focus, but already somewhat dated. We will use material from Chapters 1-17 and 19. More readings and material will be announced in the course or on request. |
| Anmeldung zur Lehrveranstaltung | Registration: Please enroll in the Online Services (services.unibas.ch); PhD - students of other Swiss Universities first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! If you have any questions, please do not hesitate to contact the Graduate School administration at gsbe-wwz@unibas.ch. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| täglich | Siehe Einzeltermine | ||
| Bemerkungen |
The lecturers are: Dr. Benjamin Arold (ETH Zürich), Dr. Alessandra Stampi – Bombelli (ETH Zürich), Dr. Claudia Marangon (ETH Zürich) |
| Datum | Zeit | Raum |
|---|---|---|
| Montag 16.12.2024 | 12.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Dienstag 17.12.2024 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Mittwoch 18.12.2024 | 14.15-18.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Donnerstag 19.12.2024 | 12.15-16.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Freitag 20.12.2024 | 10.15-14.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Module |
Modul: Fachlich-methodische Ausbildung (Promotionsfach: Staatswissenschaften) Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät (Studienbeginn vor 01.02.2024)) |
| Leistungsüberprüfung | Leistungsnachweis |
| Hinweise zur Leistungsüberprüfung | Take-home assignment. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,1 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |