Mrinmaya Sachan: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Mrinmaya Sachan
FieldMachine Learning and Natural Language Processing
Address
Masch.Lernen und Nat.Sprachverarb.
ETH Zürich, OAT Y 22.2
Andreasstrasse 5
8092 Zürich
SWITZERLAND
E-mailmrinmaya.sachan@inf.ethz.ch
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
252-0945-13LDoctoral Seminar Machine Learning (HS21)
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 credits1SJ. M. Buhmann, N. He, A. Krause, G. Rätsch, M. Sachan
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.
263-5005-00LArtificial Intelligence in Education Information Restricted registration - show details
Number of participants limited to 75.
5 credits2V + 1U + 1AM. Sachan, T. Sinha
AbstractArtificial Intelligence (AI) methods have shown to have a profound impact in educational technologies, where the great variety of tasks and data types enable us to get benefit of AI techniques in many different ways. We will review relevant methods and applications of AI in various educational technologies, and work on problem sets and projects to solve problems in education with the help of AI.
ObjectiveThe course will be centered around exploring methodological and system-focused perspectives on designing AI systems for education and analyzing educational data using AI methods. Students will be expected to a) engage in presentations and active in-class discussion, b) work on problem-sets exemplifying the use of educational data mining techniques, and c) undertake a final course project with feedback from instructors.
ContentThe course will start with a general introduction to AI, where we will cover supervised and unsupervised learning techniques (e.g.,classification and regression models, feature selection and preprocessing of data, clustering, dimensionality reduction and text mining techniques) with a focus on application of these techniques in educational data mining. After the introduction of the basic methodologies, we will continue with the most relevant applications of AI in educational technologies (e.g., intelligent tutoring and student personalization, scaffolding open-ended discovery learning, socially-aware AI and learning at scale with AI systems). In the final part of the course, we will cover challenges associated with using AI in student facing settings.
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 / NoticeThere are no prerequisites for this class. However, it will help if the student has taken an undergraduate or graduate level class in statistics, data science or machine learning. This class is appropriate for advanced undergraduates and master students in Computer Science as well as PhD students in other departments.