Alexander Ilic: Catalogue data in Spring Semester 2023

Name	PD Dr. Alexander Ilic
Address	ETH AI Center ETH Zürich, OAT X 16 Andreasstrasse 5 8092 Zürich SWITZERLAND
E-mail	alexander.ilic@ai.ethz.ch
URL	https://ai.ethz.ch/people/alexander-ilic
Department	Computer Science
Relationship	Lecturer

Number	Title	ECTS	Hours	Lecturers
263-3300-00L	Data Science Lab	14 credits	9P	A. Ilic, 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.
Learning 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.
263-5051-00L	AI Center Projects in Machine Learning Research Last cancellation/deregistration date for this ungraded semester performance: Friday, 17 March 2023! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".	4 credits	2V + 1A	A. Ilic, N. Davoudi, M. El-Assady, F. Engelmann, S. Gashi, T. Kontogianni, A. Marx, B. Moseley, G. Ramponi, X. Shen, M. Sorbaro Sindaci
Abstract	The course will give students an overview of selected topics in advanced machine learning that are currently subjects of active research. The course concludes with a final project.
Learning objective	The overall objective is to give students a concrete idea of what working in contemporary machine learning research is like and inform them about current research performed at ETH. In this course, students will be able to get an overview of current research topics in different specialized areas. In the final project, students will be able to build experience in practical aspects of machine learning research, including research literature, aspects of implementation, and reproducibility challenges.
Content	The course will be structured as sections taught by different postdocs specialized in the relevant fields. Each section will showcase an advanced research topic in machine learning, first introducing it and motivating it in the context of current technological or scientific advancement, then providing practical applications that students can experiment with, ideally with the aim of reproducing a known result in the specific field. A tentative list of topics for this year: - fully supervised 3D scene understanding - weakly supervised 3D scene understanding - causal discovery - biological and artificial neural networks - reinforcement learning - visual text analytics - human-centered AI - representation learning. The last weeks of the course will be reserved for the implementation of the final project. The students will be assigned group projects in one of the presented areas, based on their preferences. The outcomes will be made into a scientific poster and students will be asked to present the projects to the other groups in a joint poster session.
Prerequisites / Notice	Participants should have basic knowledge about machine learning and statistics (e.g. Introduction to Machine Learning course or equivalent) and programming.
263-5055-00L	Talent Kick: From Student to Entrepreneur	3 credits	2G	V. Gropengiesser, A. Ilic
Abstract	The transfer of the latest research results into scalable start-ups creates the prerequisite forsuccessful innovations. An entrepreneurial spirit and mindset enables young leaders to navigate complex environments and bring their research into practice. Studies are the best time to develop an entrepreneurial mindset and explore the entrepreneurial career path.
Learning objective	This seminar helps aspiring student/research entrepreneurs to gain hands-on entrepreneurial experience on the path from research into practice. The examples and cases will be primarily from software, AI, and other deep-tech ventures. The seminar was created with the support of ETH AI Center and University of St. Gallen and received competitive funding from the ETH Board, Fondation Botnar, Gebert Rüf Foundation, as well as support from the ETH Foundation.
Content	After attending this course, students will be able to: ● Explain the importance and tools to form successful interdisciplinary teams ● Structure customer calls and sales pitchdecks ● Build their first prototypes and MVPs ● Find the right markets and customers to bring your research into practice ● Deal with complexity in bringing innovative / novel products into market ● Develop customer-centric business strategy ● Convince first supporters incl. Entrepreneurial mentors, first investors etc.
Prerequisites / Notice	The course is practically oriented and features guest speakers from leading start-ups. The course embraces a unique perspective combining technology and investor thinking. The seminar is structured around ten days.