Name: Third Machine Learning in High Energy Physics Summer School 2017
Start: 2017-07-17T07:00:00+01:00
End: 2017-07-23T18:00:00+01:00
Location: Reading

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

Certificates, Free Discussion (in session "Lectures")

Competition Introduction (in session "Practice: challenge & industry")

Guest Lecture (in session "Invited lectures")

Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression. (in session "Lectures")

Lecture 2. Decision Trees. Bagging. Ensembles. RandomForest. AdaBoost. GB-Reweighting. Gradient Boosting. Decorrelation of features with predictions. (in session "Lectures")

Lecture 3. Ensembling: Adaboost, Stacking. Gradient Boosting. (in session "Lectures")

Lecture 4. Hyperparameters optimization techniques and methods. Feature selection. TPE. Gaussian Processes (in session "Lectures")

Lecture 5. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation. (in session "Lectures")

Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation. (in session "Lectures")

Lecture 7. Dimensionality Reduction. PCA. LDA. LLE. TSNE. Autoencoders. (in session "Lectures")

Lecture 8 Generative Adversarial Networks, Metric Learning. (in session "Lectures")

Lecture 9. RNNs. (in session "Lectures")

Lecture 10. Why Deep Learning works. Tips & tweaks.

Seminar 1. Python data crunching: numpy, root_numpy, pandas (very short), matplotlib. (in session "Seminars")

Seminar 2. Matplotlib, Trees & Linear Models in SciKit Learn. Overfitting checks & prevention. (in session "Seminars")

Seminar 3. Ensembling: Adaboost, Stacking. Gradient Boosting. (in session "Seminars")

Seminar 4. Optimization of hyper-parameters, applications to challenge. (in session "Seminars")

Seminar 5. Computing gradient by hand. Tensorflow. Keras. Remote machine setup. Getting familiar with GPU. (in session "Seminars")

Seminar 6. Image recognition using convolutional neural networks. (in session "Seminars")

Seminar 7. PCA, Autoencoders (faces) (in session "Seminars")

Seminar 8 Generative Adversarial Networks (physics) (in session "Seminars")

Third Machine Learning in High Energy Physics Summer School 2017

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Third Machine Learning in High Energy Physics Summer School 2017

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