Ryan Cotterell: Catalogue data in Autumn Semester 2020
|Name||Prof. Dr. Ryan Cotterell|
Professur für Informatik
ETH Zürich, OAT W 13.2
|Relationship||Assistant Professor (Tenure Track)|
|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.|