Ryan Cotterell: Catalogue data in Autumn Semester 2021

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
252-2300-00L	Dependency Structures and Lexicalized Grammars 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 25.	2 credits	2S	R. Cotterell
Abstract	Dependency parsing is a fundamental task in natural language processing. This seminar explores a variety of algorithms for efficient dependency parsing and their derivatioin in a unified algebraic framework.
Learning objective	The core ideas behind the mathematics of dependency parsing are explored.
Content	Dependency Structures and Lexicalized Grammars: An Algebraic Approach
252-3005-00L	Natural Language Processing Number of participants limited to 400.	5 credits	2V + 2U + 1A	R. Cotterell
Abstract	This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
Learning objective	The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.
Content	This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
Literature	Lectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers.
252-5051-00L	Advanced Topics in Machine Learning Number of participants limited to 40. The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.	2 credits	2S	J. M. Buhmann, R. Cotterell, J. Vogt, F. Yang
Abstract	In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.
Learning objective	The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.
Content	The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models.
Literature	The papers will be presented in the first session of the seminar.
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.