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Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 1) by Sanmay Ganguly
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Statistical Methods and Machine Learning in High Energy Physics - 2023
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- 1 Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 1) by Sanmay Ganguly
- 2 Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 2) by Sanmay Ganguly
- 3 Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 3) by Sanmay Ganguly
- 4 Lectures on Deepsets, Graph Neural Network and Transformers with.....(Lecture-5) by Sanmay Ganguly
- 5 Statistics for Data Analysis with Exercises Case 2: Normalization Error (Lecture-4) Tommaso Dorigo
- 6 Lectures on Deepsets, Graph Neural Network and Transformers with appl..(Lecture-6) by Sanmay Ganguly
- 7 Differentiable Programming for End-to-end Optimization of Experiments (Lecture-5) by Tommaso Dorigo
- 8 Lectures on Deepsets, Graph Neural Network and Transformers with App..(Lecture-7) by Sanmay Ganguly
- 9 Machine Learning for LHC Theory (Lecture 1) by Tilman Plehn
- 10 Differentiable Programming for End-to-end Optimization of Experiments (Lecture 6) by Tommaso Dorigo
- 11 (Lecture 1) by Elham E Khoda & Aishik Ghosh
- 12 Differentiable Programming for End-to-End Optimization of Experiments (Lecture 7) by Tommaso Dorigo
- 13 Lectures on Deepsets, Graph Neural Network and Transformers.. (Lecture 9) by Sanmay Ganguly
- 14 Neural Simulation-Based Inference (Lecture 1) by Elham E Khoda & Aishik Ghosh
- 15 Neural Simulation-based Inference (Lecture 2) by Elham E Khoda & Aishik Ghosh
- 16 Neural Simulation-based Inference (Lecture 5) by Elham E Khoda & Aishik Ghosh
- 17 Generative Models by Elham E Khoda & Aishik Ghosh
- 18 Generative Models by Elham E Khoda & Aishik Ghosh
- 19 Generative Models Application by Elham E Khoda & Aishik Ghosh
- 20 Machine Learning in High Energy Physics by Michael Kagan
- 21 Anomaly Detection with Autoencoder by Tanmoy Modak
- 22 Project Presentation (Session 2)
- 23 Machine-Learned Symmetries by Konstantin Matchev
- 24 Machine Learning for LHC Theory (Lecture 2) by Tilman Plehn
- 25 ML4Higgs: success and Future Prospects by Nick Smith
- 26 Statistics For Data Analysis with Exercises (Lecture 1) by Tommaso Dorigo
- 27 Point Estimation Combining Measurements and Fitting (Lecture 2) by Tommaso Dorigo
- 28 Exercise with Root (Lecture 3) by Tommaso Dorigo
- 29 Bread and Butter ML in HEP by Elham E Khoda & Aishik Ghosh
- 30 Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 4) by Sanmay Ganguly
