Professors and courses
![irdta-deeplearn-sean-benson](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-sean-benson.jpg)
Sean Benson
[intermediate] Deep Learning for a Better Understanding of Cancer
![irdta-deeplearn-thomas-breuel](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-thomas-breuel.jpg)
Thomas Breuel
[intermediate/advanced] Large Scale Deep Learning and Self-Supervision in Vision and NLP
![irdta-deeplearn-hao-chen](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-hao-chen.jpg)
Hao Chen
[introductory/intermediate] Label-Efficient Deep Learning for Medical Image Analysis [virtual]
![irdta-deeplearn-jianlin-cheng](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-jianlin-cheng.jpg)
Jianlin Cheng
[introductory/intermediate] Deep Learning for Bioinformatics
![Nadya Chernyavskaya](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/05/Nadya-Chernyavskaya-e1651673827634-292x292.jpg)
Nadya Chernyavskaya
[intermediate] Graph Networks for Scientific Applications with Examples from Particle Physics
![Efstratios Gavves](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/05/Efstratios-Gavves-e1652731706393.jpg)
Efstratios Gavves
[advanced] Advanced Deep Learning [virtual]
![irdta-deeplearn-quanquan-gu](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-quanquan-gu.jpg)
Quanquan Gu
[intermediate/advanced] Benign Overfitting in Machine Learning: From Linear Models to Neural Networks
![irdta-deeplearn-jiawei-han](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-jiawei-han.jpg)
Jiawei Han
[advanced] Text Mining and Deep Learning: Exploring the Power of Pretrained Language Models
![irdta-deeplearn-awni-hannun](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-awni-hannun.jpg)
Awni Hannun
[intermediate] An Introduction to Speech Recognition and Weighted Finite-State Automata [virtual]
![Tin Kam Ho](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/03/Tin-Kam-Ho-1-scaled-e1649100990464-292x292.jpg)
Tin Kam Ho
[introductory/intermediate] Deep Learning Applications in Natural Language Understanding
![irdta-deeplearn-timothy-hospedales](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-timothy-hospedales.jpg)
Timothy Hospedales
[intermediate/advanced] Deep Meta-Learning
![irdta-deeplearn-shih-chieh-hsu](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-shih-chieh-hsu.jpg)
Shih-Chieh Hsu
[intermediate/advanced] Real-Time Artificial Intelligence for Science and Engineering
![irdta-deeplearn-tatiana-likhomanenko](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-tatiana-likhomanenko.jpg)
Tatiana Likhomanenko
[intermediate/advanced] Self-, Weakly-, Semi-Supervised Learning in Speech Recognition [virtual]
![irdta-deeplearn-othmane-rifki](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-othmane-rifki.jpg)
Othmane Rifki
[introductory/advanced] Speech and Language Processing in Modern Applications
![irdta-deeplearn-mayank-vatsa](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-mayank-vatsa.jpg)
Mayank Vatsa
[introductory/intermediate] Small Sample Size Deep Learning [virtual]
![Yao Wang](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/03/Yao-Wang-1-e1651651346742-292x292.jpg)
Yao Wang
[introductory/intermediate] Deep Learning for Computer Vision
![irdta-deeplearn-zichen-wang](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-zichen-wang.jpg)
Zichen Wang
[introductory/intermediate] Graph Machine Learning for Healthcare and Life Sciences
![irdta-deeplearn-alper-yilmaz](https://deeplearn.irdta.eu/2022au/wp-content/uploads/sites/5/2022/02/irdta-deeplearn-alper-yilmaz.jpg)
Alper Yilmaz
[introductory/intermediate] Deep Learning and Deep Reinforcement Learning for Geospatial Localization