David Shih - Introduction to Generative Models and Anomaly Detection

Deepak Kar - ML applications in experiments

Stefano Forte - Regression Networks: Precision and Uncertainty Estimation Lectures plus tutorials, you need a laptops to connect to Google colab, to use python in the browser.

Claudius Krause - Generative Models at the LHC Lectures with tutorials, you need a laptop with Google colab, no packages needed.

David Shih - Introduction to Generative Models and Anomaly Detection Vanilla GAN demo: https://colab.research.google.com/drive/1afdNrrDL3lWoqOsyNceYS1V1u2Nlu-YI?usp=sharing WGAN-GP demo: https://colab.research.google.com/drive/1WNjhG3Mwx8JBa2mMreV_uiOZsuSLWiQh?usp=sharing

Stefano Forte - Regression Networks: Precision and Uncertainty Estimation link to this hands-on session: https://colab.research.google.com/drive/1S4BAH9H_4js6pMy-6XigdF8k9Rawdxad?usp=sharing link to all hands-on sessions: https://github.com/tgiani/MITP_23_tutorials

Claudius Krause - Generative Models at the LHC tutorial on colab with gaps: https://colab.research.google.com/drive/1pT_VoOGdFeoGINd9rsBhwgyTq059YbTq?usp=sharing tutorial on colab complete: https://colab.research.google.com/drive/1gpfXlRurKgDxHs0jDSygf4EwBxxRVh8o?usp=sharing Before running the notebooks: save a copy in your drive by clicking on 'file' and 'save copy in drive' at the top, then work with your copy!

Deepak Kar - ML applications in experiments

David Shih - Introduction to Generative Models and Anomaly Detection Anomaly Detection with Autoencoders Demo: https://colab.research.google.com/drive/1574RMV9hwkdTveerz_Z5WT1s1zt0Gz1I?usp=sharing VAE on MNIST Demo: https://colab.research.google.com/drive/1VcwGsGvD4B-mF1rh3WRtHlS-1zcNWF3M?usp=sharing

Stefano Forte - Regression Networks: Precision and Uncertainty Estimation

Deepak Kar - ML applications in experiments

Huilin Qu - Graph Neural Networks for Experimental Particle Physics

Huilin Qu - Graph Neural Networks for Experimental Particle Physics

David Shih - Introduction to Generative Models and Anomaly Detection Normalizing Flow demo (courtesy of Sung Hak Lim): https://drive.google.com/drive/folders/1P7-jfFn9PEJDOKWFjCxvwmHjISjci3ml

Claudius Krause - Generative Models at the LHC

Stefano Forte - Regression Networks: Precision and Uncertainty Estimation

Stefano Forte - Regression Networks: Precision and Uncertainty Estimation

Huilin Qu - Graph Neural Networks for Experimental Particle Physics

David Shih - Introduction to Generative Models and Anomaly Detection Anomaly Detection Tutorial -- Enhancing the Bump Hunt: https://colab.research.google.com/drive/1APTpBJ2GEIZ1mZen1jZehyDpqD5utUld?usp=sharing

Jim Halverson - Learning, Neural Networks, and Field Theory

Claudius Krause - Generative Models at the LHC

Ullrich Köthe - 4 types of generative models

Eilam Gross - from GNNs and Attention to Particle Flow

Jim Halverson - Learning, Neural Networks, and Field Theory

Eilam Gross - from GNNs and Attention to Particle Flow

Lukas Heinrich - Differentiable Programming

Jim Halverson - Learning, Neural Networks, and Field Theory

Sven Krippendorf - ML for string theory and mathematical structures

Ullrich Köthe - 4 types of generative models

Lukas Heinrich - Differentiable Programming

Eilam Gross - from GNNs and Attention to Particle Flow

Ullrich Köthe - 4 types of generative models

Lukas Heinrich - Differentiable Programming

Sven Krippendorf - ML for string theory and mathematical structures

Ullrich Köthe - 4 types of generative models

Sven Krippendorf - ML for string theory and mathematical structures

Ullrich Köthe - 4 types of generative models

Claudius Krause - Generative Models at the LHC Hands-on Session on Event Generation: https://colab.research.google.com/drive/1lcRN3e7HfWzhsEt_3lTK3w7YNm5wmcre?usp=sharing Hands-on Session on Uncertainties and Unfolding: https://colab.research.google.com/drive/1wq5N5w6RjvVu2tlblzeab-AATSzDaTGh?usp=sharing

Caroline Heneka - Astrophysics & ML/DL

Michael Spannowsky - Quantum Computing and Quantum ML Lectures plus tutorials. For the latter you need your laptop with python3, jupyter, matplotlib, qiskit, pennylane (for now)

Luigi DelDebbio - Field Theories and ML

Miles Cranmer - Interpretable Machine Learning

Veronica Sanz - Sequential Data Link to simple notebook with an LSTM in pytorch https://colab.research.google.com/drive/1s48ajxt8fBne72qxnp5KTBV_kyNwbKM1?usp=sharing Copy it and modify it as you want. Link to M6 competition dataset and README https://www.dropbox.com/sh/w5c7xdagtymkqpp/AADyUJaFC191sTZUSY0zwDzAa?dl=0 and the webpage of the competition, in case you would like to test your skills there https://m6competition.com/

Michael Spannowsky - Quantum Computing and Quantum ML

Luigi DelDebbio - Field Theories and ML

Miles Cranmer - Interpretable Machine Learning

Michael Kagan - Simulation-based Inference

Michael Spannowsky - Quantum Computing and Quantum ML

Luigi Favaro & Theo Heimel - Bayesian Neural Networks lecture + hands-on session

Luigi DelDebbio - Field Theories and ML

Veronica Sanz - Sequential Data

Michael Spannowsky - Quantum Computing and Quantum ML

Michael Kagan - Simulation-based Inference

Caroline Heneka - Astrophysics & ML/DL

Michael Spannowsky - Quantum Computing and Quantum ML

Michael Kagan - Neural Fields

Veronica Sanz - Sequential Data