Skip to content
Menu
Technical University of Munich
Technical University of Munich

Seminar: Selected Topics in Machine Learning Research

(IN2107, IN4872)

Application

IMPORTANT! To apply fill out a google form AND register via the matching system! Note that in order to allow a maximum number of students to attend courses, it is only possible to apply for either the Seminar or the ML Lab course.

Schedule

Prerequisites

This seminar is intended for Master's students only. You should have attended (and passed) the Machine Learning lecture (IN2064). Having attended Machine Learning for Graphs and Sequential Data (IN2323, formerly Mining Massive Datasets) 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.

Possible topics

There are more topics than students, so there should be plenty of choice for everyone.

  • Sparse Neural Networks
  • Bayesian Neural Networks
  • (Predictive) Uncertainty Estimation
  • OOD and Distribution shift detection
  • Transfer Learning
  • Adversarial Attacks
  • Robustness
  • Causality in Deep Learning
  • Disentangled Representation Learning
  • Hierarchical Representation Learning
  • Multi-scale Learning on Graphs
  • Graph Pooling
  • Knowledge Graph Inference
To top