Mrinmaya Sachan: Catalogue data in Spring Semester 2022 |
| Name | Prof. Dr. Mrinmaya Sachan |
| Field | Machine Learning and Natural Language Processing |
| Address | Masch.Lernen und Nat.Sprachverarb. ETH Zürich, OAT Y 22.2 Andreasstrasse 5 8092 Zürich SWITZERLAND |
| mrinmaya.sachan@inf.ethz.ch | |
| Department | Computer Science |
| Relationship | Assistant Professor (Tenure Track) |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 252-0945-14L | Doctoral Seminar Machine Learning (FS22) 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 credits | 1S | N. He, M. Sachan, A. Krause, G. Rätsch | |
| Abstract | An 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. | ||||
| Learning objective | The 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 / Notice | This 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. | ||||
| 263-5000-00L | Computational Semantics for Natural Language Processing  | 6 credits | 2V + 1U + 2A | M. Sachan | |
| Abstract | This 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. | ||||
| Learning objective | The 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. | ||||
| Content | We 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 notes | Lecture slides will be made available at the course Web site. | ||||
| Literature | No textbook is required, but there will be regularly assigned readings from research literature, linked to the course website. | ||||
| Prerequisites / Notice | The 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. | ||||