David Steurer: Catalogue data in Spring Semester 2018

Name	Prof. Dr. David Steurer
Field	Theoretical Computer Science
Address	Professur Theoretische Informatik ETH Zürich, OAT Z 22.2 Andreasstrasse 5 8092 Zürich SWITZERLAND
E-mail	david.steurer@inf.ethz.ch
URL	https://www.dsteurer.org
Department	Computer Science
Relationship	Associate Professor

Number	Title	ECTS	Hours	Lecturers
261-5110-00L	Optimization for Data Science	8 credits	3V + 2U + 2A	B. Gärtner, D. Steurer
Abstract	This course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science.
Objective	Understanding the theoretical and practical aspects of relevant optimization methods used in data science. Learning general paradigms to deal with optimization problems arising in data science.
Content	This course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science. In the first part of the course, we will discuss how classical first and second order methods such as gradient descent and Newton's method can be adapated to scale to large datasets, in theory and in practice. We also cover some new algorithms and paradigms that have been developed specifically in the context of data science. The emphasis is not so much on the application of these methods (many of which are covered in other courses), but on understanding and analyzing the methods themselves. In the second part, we discuss convex programming relaxations as a powerful and versatile paradigm for designing efficient algorithms to solve computational problems arising in data science. We will learn about this paradigm and develop a unified perspective on it through the lens of the sum-of-squares semidefinite programming hierarchy. As applications, we are discussing non-negative matrix factorization, compressed sensing and sparse linear regression, matrix completion and phase retrieval, as well as robust estimation.
Prerequisites / Notice	As background, we require material taught in the course "252-0209-00L Algorithms, Probability, and Computing". It is not necessary that participants have actually taken the course, but they should be prepared to catch up if necessary.
263-4110-00L	Interdisciplinary Algorithms Lab In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.	5 credits	2P	A. Steger, D. Steurer, J. Lengler
Abstract	In this course students will develop solutions for algorithmic problems posed by researchers from other fields.
Objective	Students will learn that in order to tackle algorithmic problems from an interdisciplinary or applied context one needs to combine a solid understanding of algorithmic methodology with insights into the problem at hand to judge which side constraints are essential and which can be loosened.
Prerequisites / Notice	Students will work in teams. Ideally, skills of team members complement each other. Interested Bachelor students can apply for participation by sending an email to steger@inf.ethz.ch explaining motivation and transcripts.