Elliott Ash: Katalogdaten im Frühjahrssemester 2021

NameHerr Prof. Dr. Elliott Ash
LehrgebietRecht, Ökonomie und Datenwissenschaften
Adresse
Recht, Ökonomie und Datenwiss.
ETH Zürich, IFW E 47.1
Haldeneggsteig 4
8092 Zürich
SWITZERLAND
Telefon+41 44 633 89 62
E-Mailelliott.ash@gess.ethz.ch
DepartementGeistes-, Sozial- und Staatswissenschaften
BeziehungAssistenzprofessor

NummerTitelECTSUmfangDozierende
851-0739-01LSequencing Legal DNA: NLP for Law and Political Economy
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
3 KP2VE. Ash
KurzbeschreibungThis course explores the application of natural language processing techniques to texts in law, politics, and the news media.
LernzielStudents will be introduced to a broad array of tools in natural language processing (NLP). They will learn to evaluate and apply NLP tools to a variety of problems. The applications will focus on social-science contexts, including law, politics, and the news media. Topics include text classification, topic modeling, transformers, model explanation, and bias in language.
InhaltNLP technologies have the potential to assist judges and other decision-makers by making tasks more efficient and consistent. On the other hand, language choices could be biased toward some groups, and automated systems could entrench those biases.

We will explore the use of NLP for social science research, not just in the law but also in politics, the economy, and culture. We will explore, critique, and integrate the emerging set of tools for debiasing language models and think carefully about how notions of fairness should be applied in this domain.
Voraussetzungen / BesonderesSome programming experience in Python is required, and some experience with NLP is highly recommended.
851-0739-02LSequencing Legal DNA: NLP for Law and Political Economy (Course Project)
This is the optional course project for "Building a Robot Judge: Data Science for the Law."

Please register only if attending the lecture course or with consent of the instructor.

Some programming experience in Python is required, and some experience with text mining is highly recommended.
2 KP2VE. Ash
KurzbeschreibungThis is the companion course for extra credit for a course project, for the course "Sequencing Legal DNA: NLP for Law and Political Economy".
LernzielStudents will be introduced to a broad array of tools in natural language processing (NLP). They will learn to evaluate and apply NLP tools to a variety of problems. The applications will focus on social-science contexts, including law, politics, and the news media. Topics include text classification, topic modeling, transformers, model explanation, and bias in language.
860-0033-00LBig Data for Public Policy Information Belegung eingeschränkt - Details anzeigen
Nur für Masterstudierende und Doktorierende.
3 KP2GE. Ash, M. Guillot
KurzbeschreibungThis course provides an introduction to big data methods for public policy analysis. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors.
LernzielMany policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions.
InhaltMany policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions. These techniques include:

-- procuring big datasets, especially through web scraping or API interfaces, including social media data;
-- pre-processing and dimension reduction of massive datasets for tractable computation;
-- machine learning for predicting outcomes, including how to select and tune the model, evaluate model performance using held-out test data, and report results;
-- interpreting machine learning model predictions to understand what is going on inside the black box;
-- data visualization including interactive web apps.

Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors.