Ryan Cotterell: Catalogue data in Spring Semester 2022

Name	Prof. Dr. Ryan Cotterell
Field	Computer Science
Address	Professur für Informatik ETH Zürich, OAT W 13.2 Andreasstrasse 5 8092 Zürich SWITZERLAND
E-mail	ryan.cotterell@inf.ethz.ch
Department	Computer Science
Relationship	Assistant Professor (Tenure Track)

Number	Title	ECTS	Hours	Lecturers
252-2310-00L	Understanding Context-Free Parsing Algorithms The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. Number of participants limited to 24.	2 credits	2S	R. Cotterell
Abstract	Parsing context-free grammars is a fundamental problem in natural language processing and computer science more broadly. This seminar will explore a classic text that unifies many algorithms for parsing in one framework.
Learning objective	Sikkel's notion of parsing schemata is explored in depth. The students should take away an understanding and fluency with these ideas.
Content	Parsing Schemata: A Framework for Specification and Analysis of Parsing Algorithms
263-3300-00L	Data Science Lab Only for Data Science MSc.	14 credits	9P	C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang
Abstract	In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Learning objective	The goal of this class if for students to gain experience of dealing with data science and machine learning applications "in the wild". Students are expected to go through the full process starting from data cleaning, modeling, execution, debugging, error analysis, and quality/performance refinement.
Prerequisites / Notice	Prerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing.
263-5352-00L	Advanced Formal Language Theory	5 credits	2V + 1U + 1A	R. Cotterell
Abstract	This course serves as an introduction to various advanced topics in formal language theory.
Learning objective	The objective of the course is to learn and understand a variety of topics in advanced formal language theory.
Content	This course serves as an introduction to various advanced topics in formal language theory. The primary focus of the course is on weighted formalisms, which can easily be applied in machine learning. Topics include finite-state machines as well as the algorithms that are commonly used for their manipulation. We will also cover weighted context-free grammars, weighted tree automata, and weighted mildly context-sensitive formalisms.