Elliott Ash: Catalogue data in Spring Semester 2021

Name	Prof. Dr. Elliott Ash
Field	Law, Economics and Data Science
Address	Recht, Ökonomie und Datenwiss. ETH Zürich, IFW E 47.1 Haldeneggsteig 4 8092 Zürich SWITZERLAND
Telephone	+41 44 633 89 62
E-mail	elliott.ash@gess.ethz.ch
Department	Humanities, Social and Political Sciences
Relationship	Associate Professor

Number	Title	ECTS	Hours	Lecturers
851-0739-01L	Sequencing Legal DNA: NLP for Law and Political Economy Particularly suitable for students of D-INFK, D-ITET, D-MTEC	3 credits	2V	E. Ash
Abstract	This course explores the application of natural language processing techniques to texts in law, politics, and the news media.
Learning objective	Students 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.
Content	NLP 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.
Prerequisites / Notice	Some programming experience in Python is required, and some experience with NLP is highly recommended.
851-0739-02L	Sequencing 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 credits	2V	E. Ash
Abstract	This is the companion course for extra credit for a course project, for the course "Sequencing Legal DNA: NLP for Law and Political Economy".
Learning objective	Students 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.
860-0033-00L	Big Data for Public Policy Only for Master students and PhD students.	3 credits	2G	E. Ash, M. Guillot
Abstract	This 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.
Learning objective	Many 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.
Content	Many 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.