Ryan Cotterell: Catalogue data in Autumn Semester 2020

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
252-3005-00L	Natural Language Processing Number of participants limited to 200.	5 credits	2V + 1U + 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.
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	Jacob Eisenstein: Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)
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.
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.