Mrinmaya Sachan: Catalogue data in Spring Semester 2021

Name Prof. Dr. Mrinmaya Sachan
FieldMachine Learning and Natural Language Processing
Address
Masch.Lernen und Nat.Sprachverarb.
ETH Zürich, CAB E 61.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telephone+41 44 632 31 07
E-mailmrinmaya.sachan@inf.ethz.ch
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
252-0945-12LDoctoral Seminar Machine Learning (FS21)
Only for Computer Science Ph.D. students.

This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar.
2 credits1SN. He, M. Sachan, J. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch
AbstractAn essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
ObjectiveThe seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills.
Prerequisites / NoticeThis doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab.
263-5000-00LComputational Semantics for Natural Language Processing Information Restricted registration - show details
Limited number of participants: 80. Last cancellation/deregistration date for this graded semester performance: Friday, 26 March 2021! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".
6 credits2V + 1U + 2AM. Sachan
AbstractThis course presents an introduction to Natural language processing (NLP) with an emphasis on computational semantics i.e. the process of constructing and reasoning with meaning representations of natural language text.
ObjectiveThe objective of the course is to learn about various topics in computational semantics and its importance in natural language processing methodology and research. Exercises and the project will be key parts of the course so the students will be able to gain hands-on experience with state-of-the-art techniques in the field.
ContentWe will take a modern view of the topic, and focus on various statistical and deep learning approaches for computation semantics. We will also overview various primary areas of research in language processing and discuss how the computational semantics view can help us make advances in NLP.
Lecture notesLecture slides will be made available at the course Web site.
LiteratureNo textbook is required, but there will be regularly assigned readings from research literature, linked to the course website.
Prerequisites / NoticeThe student should have successfully completed a graduate level class in machine learning (252-0220-00L), deep learning (263-3210-00L) or natural language processing (252-3005-00L) before. Similar courses from other universities are acceptable too.