Alexander Ilic: Katalogdaten im Herbstsemester 2023 |
| Name | Herr PD Dr. Alexander Ilic |
| Adresse | ETH AI Center ETH Zürich, OAT X 16 Andreasstrasse 5 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 35 84 |
| alexander.ilic@ai.ethz.ch | |
| URL | https://ai.ethz.ch/people/alexander-ilic |
| Departement | Informatik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-3300-00L | Data Science Lab   Only for Data Science MSc, Programme Regulations 2017. | 14 KP | 9P | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-3300-10L | Data Science Lab Only for Data Science MSc, Programme Regulations 2023. | 10 KP | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-5053-00L | Technology Investing | 2 KP | 3S | A. Ilic, C. Jurytko | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Venture Capital is important to fund big transformational ideas and is often misunderstood by tech or research entrepreneurs. This lecture immerses participants in the role of a Venture Capitalist (VC) to learn from experienced entrepreneurs and investors. In small teams, you work on a case of a real start-up and defend the case in a simulated investment committee consisting of experienced VCs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After attending this course, students will be able to: - Explain the differences between VC and founder thinking - Evaluate if a start-up is suited for venture capital (“VC readiness”) - Evaluate founder friendliness of term sheets - Determine funding needs & strategy for a start-up from research to first round - Write and evaluate an investment memo | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course is practically oriented and features guest speakers from leading venture capital firms and start-ups. The course embraces a unique perspective combining technology and investor thinking. The seminar is structured around five days with the following themes. The detailed program is listed here: https://bit.ly/techinvesting23 The macro picture. Why does venture capital exist? What are major tech break-through areas and their disruptive potential? We also review the differences in the US and European perspective as well as developments towards more impact and diversity conscious funds. A peek into the mind of a VC. How to build a successful VC? Learn what key factors & processes required to build a successful venture capital company. This includes strategic decisions for investment thesis, structure of a fund, portfolio economics, valuation & ownership targets, cap table. In addition, we introduce the fundamentals of the investment process (including due diligence, term sheets, and deal memo) as well as portfolio management. The founder’s perspective. Why should you raise venture capital and how? Learn to evaluate the founder friendliness of terms, company approach, strategic decisions, negotiation and valuation. Fundraising types. Learn about different types of funding and their implications. This includes an overview of the Swiss ecosystem and a discussion of the different types (grants, equity, loans, SAFE, crowd, …). We also include a practical session on crypto technology for modern fund-raising using launchpads and tokenized shares. Tying it all together. The last day is focused on simulating an investment committee meeting where the groups present their deal memos and discuss with the audience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 263-5054-00L | Patenting Digital Innovations | 1 KP | 2S | A. Ilic, B. Best | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | In this seminar dedicated to digital innovations, we will bust the most stubborn myths around AI software patents such as “Software/AI isn’t patentable”, “AI patents are useless because you can’t figure out if they are infringed”, and many others. We will look at how AI and software start-ups can use patents to create a strong IP position in a scalable way. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After attending this course, students will be able to: - Understand the basics of patenting in the digital space relevant for a global market - Evaluate patenting opportunities with a more differentiated view on the topic - Effectively use patents as a cost-effective part of a technology startup’s business plan - Conduct patent searches, freedom-to-operate analysis and infringement analyses - Write their first software/AI-related invention disclosure suitable for patenting | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course is focused on patenting digital innovations. It is designed for students with entrepreneurial interests that like to get a hands-on perspective on the topic of intellectual property strategies and patents. The seminar includes presentations and practical group exercises to apply the acquired knowledge in practice. Entrepreneurs and leading IP experts are joining the seminar as guest speakers for discussion of real-life examples. Topics that will be covered include: - Best practices that any AI/software startups should know about IP and patents - How investors evaluate a strong IP situation of a start-up - How to efficiently monitor competitor patent activity and obtain “FTO” - How to create an effective patent filing strategy that grows with the business - How to efficiently create AI patents while not getting distracted from the founder’s core business The course also contains a group work of a “FTO battle” where two teams compete in a freedom-to-operate analysis and individual work to write their first invention disclosure related to an AI or software topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||