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

Third Machine Learning in High Energy Physics Summer School 2017

from Monday, July 17, 2017 (7:00 AM) to Sunday, July 23, 2017 (6:00 PM)
Reading (Reading University)

: Sessions

: Talks

: Breaks

Jul 17, 2017

Jul 18, 2017

Jul 19, 2017

Jul 20, 2017

Jul 21, 2017

Jul 22, 2017

Jul 23, 2017

8:50 AM	Organisational - Ulrik Egede (Imperial College (GB)) Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) (until 9:00 AM) (Reading University)
8:50 AM	'Welcome to MLHEP' Opening Words - Ulrik Egede (Imperial College (GB)) Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) (Reading University) Egede.pdf
9:00 AM	Lectures - Alexey Artemov (Yandex) (until 10:30 AM) (Reading University)
9:00 AM	Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression. - Alexey Artemov (Yandex) (Reading University)
10:30 AM	--- Coffee break ---
10:50 AM	Practice: challenge & industry - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) (until 11:10 AM) (Reading University)
10:50 AM	Competition Introduction - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) (Reading University)
11:10 AM	Seminars - Alexander Panin (Yandex School of Data Analysis (RU)) (until 12:40 PM) (Reading University)
11:10 AM	Seminar 1. Python data crunching: numpy, root_numpy, pandas (very short), matplotlib. - Alexander Panin (Yandex School of Data Analysis (RU)) (Reading University)

9:00 AM	Lectures (until 10:30 AM) (Reading University)
9:00 AM	Lecture 3. Ensembling: Adaboost, Stacking. Gradient Boosting. (Reading University)
10:30 AM	--- Coffee break ---
11:00 AM	Seminars (until 12:30 PM) (Reading University)
11:00 AM	Seminar 3. Ensembling: Adaboost, Stacking. Gradient Boosting. (Reading University)

9:00 AM

Free day (until 9:00 PM) (Reading University)

9:00 AM	Lectures (until 10:30 AM) (Reading University)
9:00 AM	Lecture 5. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation. (Reading University)
10:30 AM	--- Coffee break ---
11:00 AM	Seminars (until 12:30 PM) (Reading University)
11:00 AM	Seminar 5. Computing gradient by hand. Tensorflow. Keras. Remote machine setup. Getting familiar with GPU. (Reading University)

9:00 AM	Practice: challenge & industry (until 10:30 AM) (Reading University)
10:30 AM	--- Coffee break ---
11:00 AM	Practice: challenge & industry (until 12:30 PM) (Reading University)

9:00 AM	Lectures (until 10:30 AM) (Reading University)
9:00 AM	Lecture 8 Generative Adversarial Networks, Metric Learning. (Reading University)
10:30 AM	--- Coffee break ---
11:00 AM	Seminars (until 12:30 PM) (Reading University)
11:00 AM	Seminar 8 Generative Adversarial Networks (physics) (Reading University)

9:00 AM	Lecture 10. Why Deep Learning works. Tips & tweaks. (Reading University)
10:30 AM	--- Break ---
11:00 AM	Practice: challenge & industry (until 12:00 PM) (Reading University)

12:40 PM	Organisational (until 2:00 PM) (Reading University)
2:00 PM	Lectures - Alexey Artemov (Yandex) (until 3:30 PM) (Reading University)
2:00 PM	Lecture 2. Decision Trees. Bagging. Ensembles. RandomForest. AdaBoost. GB-Reweighting. Gradient Boosting. Decorrelation of features with predictions. - Alexey Artemov (Yandex) (Reading University)
3:30 PM	--- Coffee break ---
3:50 PM	Seminars - Alexander Panin (Yandex School of Data Analysis (RU)) (until 5:20 PM) (Reading University)
3:50 PM	Seminar 2. Matplotlib, Trees & Linear Models in SciKit Learn. Overfitting checks & prevention. - Alexander Panin (Yandex School of Data Analysis (RU)) (Reading University)
7:00 PM	Organisational (until 8:30 PM) (Reading University)

12:30 PM	Organisational (until 1:30 PM) (Reading University)
1:30 PM	Lectures (until 3:00 PM) (Reading University)
1:30 PM	Lecture 4. Hyperparameters optimization techniques and methods. Feature selection. TPE. Gaussian Processes (Reading University)
3:00 PM	--- Coffee break ---
3:30 PM	Seminars (until 5:00 PM) (Reading University)
3:30 PM	Seminar 4. Optimization of hyper-parameters, applications to challenge. (Reading University)
5:00 PM	Organisational (until 5:15 PM) (Reading University)
5:15 PM	Invited lectures (until 6:45 PM) (Reading University)

12:30 PM	Organisational (until 1:30 PM) (Reading University)
1:30 PM	Lectures (until 3:00 PM) (Reading University)
1:30 PM	Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation. (Reading University)
3:00 PM	--- Coffee break ---
3:30 PM	Seminars (until 5:00 PM) (Reading University)
3:30 PM	Seminar 6. Image recognition using convolutional neural networks. (Reading University)
5:00 PM	Organisational (until 5:15 PM) (Reading University)
5:15 PM	Invited lectures (until 6:45 PM) (Reading University)
5:15 PM	Guest Lecture - Noel Dawe (University of Melbourne (AU)) (Reading University) deepjets.pdf

12:30 PM	Organisational (until 1:30 PM) (Reading University)
1:30 PM	Lectures (until 3:00 PM) (Reading University)
1:30 PM	Lecture 7. Dimensionality Reduction. PCA. LDA. LLE. TSNE. Autoencoders. (Reading University)
3:00 PM	--- Coffee break ---
3:30 PM	Seminars (until 5:00 PM) (Reading University)
3:30 PM	Seminar 7. PCA, Autoencoders (faces) (Reading University)
5:00 PM	Organisational (until 5:15 PM) (Reading University)
5:15 PM	Invited lectures (until 6:45 PM) (Reading University)
7:00 PM	--- Dinner ---

12:30 PM	Organisational (until 1:30 PM) (Reading University)
1:30 PM	Lectures (until 4:00 PM) (Reading University)
1:30 PM	Lecture 9. RNNs. (Reading University)
3:00 PM	--- Coffee break ---
3:30 PM	Certificates, Free Discussion (Reading University)

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