Name: Fifth Machine Learning in High Energy Physics Summer School 2019
Start: 2019-07-01T07:30:00+02:00
End: 2019-07-10T18:30:00+02:00
Location: DESY

'Welcome to MLHEP' Opening Words (in session "Organisational")

Bayesian methods 1

Bayesian methods 2

Bayesian neural networks 1

Bayesian neural networks 2

Big Picture: AI/ML in HEP and in the wild (in session "Lectures")

Competition 2 intro + Competition seminar

Competition Introduction (in session "Practice")

Competition time

DIRC-GAN

Evening event: Artificial intelligence: Past, Present and Future

Graph Network in High Energy Physics

Lecture: Black-box optimization: Bayesian Optimization, Variational Optimization, Adversarial Optimization

Lecture: Causal inference

Lecture: Dark Machines

Lecture: Decision tree-based models

Lecture: GAN advanced topics: WGAN, Cramer GAN

Lecture: Generative Models (GM); GMM; Probability density estimation (kernel, fitting); RBM; JS GAN; pivot (in session "Lecture")

Lecture: Intro to Deep learning; Convolutional Neural Networks

Lecture: Introduction into Neural Network models; Multi-layered Perceptron; Backpropagation

Lecture: Likelihood free inference

Lecture: Linear models, regression classification (in session "Lecture")

Lecture: Machine Learning and Computer Vision for Particle Imaging Detectors in Neutrino Experiments

Lecture: Probabilistic programming

Lecture: Sequence Modelling

Lecture: Unsupervised Machine Learning

Lecture: Using Bayesian Framework to identify Risks in Project Management

Machine Learning with Multiscale Dataflow Computing for High Energy Physics-1

Machine Learning with Multiscale Dataflow Computing for High Energy Physics-2

Organizational info

School wrap up

Seminar: Causal inference

Seminar: Clustering

Seminar: Computing gradient by hand. Pytorch.

Seminar: Ensembles of models; bagging, boosting, random forest

Seminar: Figures of Merit, overfitting (MLE vs MAP vs AP) (in session "Seminar")

Seminar: Image recognition using convolutional neural networks

Seminar: Independence of NN classifier from a continuous parameter

Seminar: Learning to pivot (in session "Seminar")

Seminar: NN Hyper-parameter optimization, comet

Seminar: NN interpretation. Tips & tweaks. (in session "Seminar")

Seminar: Probabilistic programming

Seminar: RNN: GRU + LSTM + Jet recognition

Seminar: Tuning PYTHIA

Fifth Machine Learning in High Energy Physics Summer School 2019

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Fifth Machine Learning in High Energy Physics Summer School 2019

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