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Technische Universität München
Technische Universität München

Seminar: Selected Topics in Machine Learning Research

(IN2107, IN4872)

Application

The pre-course meeting with information regarding the course format, possible topics etc. is scheduled for February 5, 2024 3pm in room 8120.05.012 or 8120.05.032 (MDSI, GALILEO). The meeting will take place together with the information event for our lab course and the final presentations of this semester's groups. Slides will be made available on this page afterwards.

Note that you have to register via the matching system and fill out our application form to apply for a spot!

Info Event Slides can be found here.

Schedule

  • Pre-course meeting: February 5, 2024 3pm in room 8120.05.012 or 8120.05.032 (MDSI, GALILEO) (info event slides: )
  • Application deadline (matching system & form): February 14, 2024
  • Kick-off meeting: TBA
  • Final presentations: TBA

Prerequisites

This seminar is intended for Master's students only. You should have attended (and passed) the Machine Learning lecture (IN2064). Having passed Machine Learning for Graphs and Sequential Data (IN2323) or Advanced Machine Learning: Deep Generative Models (CIT4230003) is a plus.

Description

The amount of research in machine learning has grown exponentially in the last couple of years, uncovering many promising and successful research directions. In this seminar we will select and discuss a diverse set of topics of current research. This seminar will let students get acquainted with current machine learning research, let them explore new fields and ideas and let them analyze and criticize recent publications.

To do so, each student will receive 2-5 research papers which they should carefully read and analyze. Starting from these they should explore the surrounding literature and summarize their findings, criticism, and research ideas in a 4-page paper (double column). The students will then review each other's work to give valuable feedback and criticism. Finally, all students will prepare 25-minute presentations and present their work during a block seminar at the end of the semester.

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