Professors and coursesTO BE COMPLETED

Luca Benini
[intermediate/advanced] Open Hardware Platforms for Edge Machine Learning

Gustau Camps-Valls
[intermediate] AI for Earth, Climate, and Sustainability

Nitesh Chawla
[introductory/intermediate] Introduction to Representation Learning on Graphs

Daniel Cremers
[introductory/advanced] Deep Networks for 3D Computer Vision

Peng Cui
[intermediate/advanced] Stable Learning for Out-of-Distribution Generalization: Invariance, Causality and Heterogeneity

Sergei V. Gleyzer
[introductory/intermediate] Machine Learning Fundamentals and Their Applications to Very Large Scientific Data: Rare Signal and Feature Extraction, End-to-End Deep Learning, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware

Yulan He
[intermediate/advanced] Machine Reading Comprehension with Large Language Models

Frank Hutter
[intermediate/advanced] AutoML

George Karypis
[intermediate] Deep Learning Models and Systems for Real-World Graph Machine Learning

Anais Möller
[introductory/intermediate] Classification in Time-Domain Astronomy, the Challenges of Interpretability and Irregular and Very Large Data Sets

Hermann Ney
[intermediate/advanced] Machine Learning and Deep Learning for Speech & Language Technology: A Probabilistic Perspective

Massimiliano Pontil
[intermediate/advanced] Operator Learning for Dynamical Systems

Elisa Ricci
[intermediate] Continual and Adaptive Learning in Computer Vision

Xinghua Mindy Shi
[intermediate] Trustworthy Artificial Intelligence for Health and Medicine

Laurens van der Maaten
[introductory/intermediate] Introduction to Computer Vision

Danxia Xu
[introductory] Photonic Chips and Artificial Intelligence: An Interplay

James Zou
[introductory/intermediate] Large Language Models and Biomedical Applications