Ryan Cotterell: Catalogue data in Spring Semester 2022

Name Prof. Dr. Ryan Cotterell
FieldComputer Science
Professur für Informatik
ETH Zürich, OAT W 13.2
Andreasstrasse 5
8092 Zürich
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

252-2310-00LUnderstanding Context-Free Parsing Algorithms Information Restricted registration - show details
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 credits2SR. Cotterell
AbstractParsing 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.
ObjectiveSikkel's notion of parsing schemata is explored in depth. The students should take away an understanding and fluency with these ideas.
ContentParsing Schemata: A Framework for Specification and Analysis of Parsing Algorithms
263-3300-00LData Science Lab Restricted registration - show details
Only for Data Science MSc.
14 credits9PC. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang
AbstractIn 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.
ObjectiveThe 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 / NoticePrerequisites: 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.
263-5352-00LAdvanced Formal Language Theory Information 5 credits2V + 1U + 1AR. Cotterell
AbstractThis course serves as an introduction to various advanced topics in formal language theory.
ObjectiveThe objective of the course is to learn and understand a variety of topics in advanced formal language theory.
ContentThis 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.