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09:00
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Welcome from the Organisers
-
Stefan Prestel
Malin Sjödahl
(Zoom)
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09:05
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Historical Perspective
- Prof.
Carsten Petersson
(Lund University)
(Zoom)
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09:30
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--- Question Break ---
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09:35
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General introduction
-
Mattias Ohlsson
(Zoom)
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10:00
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--- Question Break ---
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10:05
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General introduction (continued)
-
Mattias Ohlsson
(Zoom)
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10:20
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Introduction to Machine Learning
-
Mattias Ohlsson
(Zoom)
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10:35
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--- Feedback and coffee ---
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10:55
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Introduction to Machine Learning (continued)
-
Mattias Ohlsson
(Zoom)
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11:10
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The MLP Architecture
-
Mattias Ohlsson
(Zoom)
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11:25
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--- Question Break ---
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11:30
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The MLP Architecture (continued)
-
Mattias Ohlsson
(Zoom)
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09:00
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Welcome
-
The organizers
(Zoom)
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09:10
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Recurrent neural networks
-
Mattias Ohlsson
(Zoom)
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09:40
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--- Question Break ---
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09:45
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Recurrent neural networks (continued)
-
Mattias Ohlsson
(Zoom)
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10:15
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--- Coffee break ---
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10:35
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Tutorials
-
Mattias Ohlsson
Najmeh Abiri
(until 12:00)
(Zoom)
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09:00
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Welcome
-
The organizers
(Zoom)
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09:10
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Machine learning for image analysis: Recap + Deep Learning for Video and Audio
-
Niclas Danielsson
(Zoom)
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09:40
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--- Question Break ---
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09:45
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Machine learning for image analysis: Deep Learning in the Industry and Deployment Platforms
-
Niclas Danielsson
(Zoom)
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10:15
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--- Feedback and coffee ---
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10:35
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Machine learning for image analysis: Introduction to Tensorflow 2
-
Niclas Danielsson
(Zoom)
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11:05
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--- Question Break ---
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11:15
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Machine learning for image analysis: Preparing, training, visualizing.
-
Niclas Danielsson
(Zoom)
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11:45
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--- Feedback and coffee ---
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09:00
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Welcome
-
The organizers
(Zoom)
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09:10
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How to GAN LHC events
-
Anja Butter
(Zoom)
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09:40
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--- Question Break ---
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09:45
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How to GAN LHC events (continued)
-
Anja Butter
(Zoom)
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10:15
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--- Feedback and coffee ---
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10:35
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Accelerating HEP theory with ML models
-
Stefano Carrazza
(Zoom)
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11:05
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--- Question Break ---
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11:10
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Accelerating HEP theory with ML models (continued)
-
Stefano Carrazza
(Zoom)
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11:40
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--- Feedback and coffee ---
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09:00
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Welcome
-
The organizers
(Zoom)
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09:10
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Can we "machine learn" the Next Standard Model?
-
Wolfgang Waltenberger
(Zoom)
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09:40
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--- Question Break ---
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09:45
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Can we "machine learn" the Next Standard Model? (continued)
-
Wolfgang Waltenberger
(Zoom)
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10:15
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--- Feedback and coffee ---
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10:35
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Towards the autonomous machine learning fueled supply chain.
-
Malte Tichy
(Zoom)
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11:05
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--- Question Break ---
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11:10
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Towards the autonomous machine learning fueled supply chain (continued)
-
Malte Tichy
(Zoom)
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11:40
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--- Feedback and coffee ---
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12:00
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--- Free time ---
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13:30
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The CNN architecture
-
Mattias Ohlsson
(Zoom)
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14:00
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--- Question Break ---
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14:05
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The CNN architecture (continued)
-
Mattias Ohlsson
(Zoom)
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12:00
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--- Free time ---
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13:30
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Tutorials
-
Mattias Ohlsson
Najmeh Abiri
(until 15:00)
(Zoom)
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15:00
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--- Free time ---
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17:00
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ML in HEP: preliminaries
-
Ben Nachmann
(Zoom)
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17:30
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--- Question Break ---
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17:35
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Deep learning with HEP images
-
Ben Nachmann
(Zoom)
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18:05
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--- Question break ---
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18:25
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Deep learning in HEP beyond images
(Zoom)
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12:00
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--- Free time ---
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13:30
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Tutorial and Transfer Learning: Machine Learning for Image Analysis
-
Niclas Danielsson
(until 15:30)
(Zoom)
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15:30
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Free time
(until 16:45)
(Zoom)
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16:45
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ML in HEP: Likelihood-free methods for removing distortions
-
Ben Nachmann
(Zoom)
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17:15
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--- Break/Free time ---
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18:15
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ML in HEP: Generative models
-
Ben Nachmann
(Zoom)
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18:45
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--- Question break ---
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18:50
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ML in HEP: Uncertainty quantification and anomaly detection
-
Ben Nachmann
(Zoom)
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12:00
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--- Free time ---
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13:30
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Tutorial
-
Najmeh Abiri
(until 16:30)
(Zoom)
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12:00
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--- Free time ---
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13:30
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Bayesian deep probabilistic differentiable programming: A scientific approach to AI
-
Michael Green
(Zoom)
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14:00
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--- Question Break ---
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14:05
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Bayesian deep probabilistic differentiable programming: A scientific approach to AI (continued)
-
Michael Green
(Zoom)
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14:35
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--- Feedback and coffee ---
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14:55
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Outlook on ML in HEP
-
Tilman Plehn
(Zoom)
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15:55
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Farewell!
-
The organizers
(Zoom)
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