Third Machine Learning in High Energy Physics Summer School 2017

from Monday, 17 July 2017 (07:00) to Sunday, 23 July 2017 (18:00)
Reading (Reading University)

        : Sessions
    /     : Talks
        : Breaks
17 Jul 2017
18 Jul 2017
19 Jul 2017
20 Jul 2017
21 Jul 2017
22 Jul 2017
23 Jul 2017
AM
08:50
Organisational - Ulrik Egede (Imperial College (GB)) Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) (until 09:00) (Reading University)
08:50 'Welcome to MLHEP' Opening Words - Ulrik Egede (Imperial College (GB)) Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))   (Reading University)
09:00
Lectures - Alexey Artemov (Yandex) (until 10:30) (Reading University)
09:00 Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression. - Alexey Artemov (Yandex)   (Reading University)
10:30 --- Coffee break ---
10:50
Practice: challenge & industry - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) (until 11:10) (Reading University)
10:50 Competition Introduction - Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))   (Reading University)
11:10
Seminars - Alexander Panin (Yandex School of Data Analysis (RU)) (until 12:40) (Reading University)
11:10 Seminar 1. Python data crunching: numpy, root_numpy, pandas (very short), matplotlib. - Alexander Panin (Yandex School of Data Analysis (RU))   (Reading University)
09:00
Lectures (until 10:30) (Reading University)
09:00 Lecture 3. Ensembling: Adaboost, Stacking. Gradient Boosting.   (Reading University)
10:30 --- Coffee break ---
11:00
Seminars (until 12:30) (Reading University)
11:00 Seminar 3. Ensembling: Adaboost, Stacking. Gradient Boosting.   (Reading University)
09:00
Free day (until 21:00) (Reading University)
09:00
Lectures (until 10:30) (Reading University)
09:00 Lecture 5. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation.   (Reading University)
10:30 --- Coffee break ---
11:00
Seminars (until 12:30) (Reading University)
11:00 Seminar 5. Computing gradient by hand. Tensorflow. Keras. Remote machine setup. Getting familiar with GPU.   (Reading University)
09:00
Practice: challenge & industry (until 10:30) (Reading University)
10:30 --- Coffee break ---
11:00
Practice: challenge & industry (until 12:30) (Reading University)
09:00
Lectures (until 10:30) (Reading University)
09:00 Lecture 8 Generative Adversarial Networks, Metric Learning.   (Reading University)
10:30 --- Coffee break ---
11:00
Seminars (until 12:30) (Reading University)
11:00 Seminar 8 Generative Adversarial Networks (physics)   (Reading University)
09:00 Lecture 10. Why Deep Learning works. Tips & tweaks.   (Reading University)
10:30 --- Break ---
11:00
Practice: challenge & industry (until 12:00) (Reading University)
PM
12:40
Organisational (until 14:00) (Reading University)
14:00
Lectures - Alexey Artemov (Yandex) (until 15:30) (Reading University)
14:00 Lecture 2. Decision Trees. Bagging. Ensembles. RandomForest. AdaBoost. GB-Reweighting. Gradient Boosting. Decorrelation of features with predictions. - Alexey Artemov (Yandex)   (Reading University)
15:30 --- Coffee break ---
15:50
Seminars - Alexander Panin (Yandex School of Data Analysis (RU)) (until 17:20) (Reading University)
15:50 Seminar 2. Matplotlib, Trees & Linear Models in SciKit Learn. Overfitting checks & prevention. - Alexander Panin (Yandex School of Data Analysis (RU))   (Reading University)
19:00
Organisational (until 20:30) (Reading University)
12:30
Organisational (until 13:30) (Reading University)
13:30
Lectures (until 15:00) (Reading University)
13:30 Lecture 4. Hyperparameters optimization techniques and methods. Feature selection. TPE. Gaussian Processes   (Reading University)
15:00 --- Coffee break ---
15:30
Seminars (until 17:00) (Reading University)
15:30 Seminar 4. Optimization of hyper-parameters, applications to challenge.   (Reading University)
17:00
Organisational (until 17:15) (Reading University)
17:15
Invited lectures (until 18:45) (Reading University)
12:30
Organisational (until 13:30) (Reading University)
13:30
Lectures (until 15:00) (Reading University)
13:30 Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation.   (Reading University)
15:00 --- Coffee break ---
15:30
Seminars (until 17:00) (Reading University)
15:30 Seminar 6. Image recognition using convolutional neural networks.   (Reading University)
17:00
Organisational (until 17:15) (Reading University)
17:15
Invited lectures (until 18:45) (Reading University)
17:15 Guest Lecture - Noel Dawe (University of Melbourne (AU))   (Reading University)
12:30
Organisational (until 13:30) (Reading University)
13:30
Lectures (until 15:00) (Reading University)
13:30 Lecture 7. Dimensionality Reduction. PCA. LDA. LLE. TSNE. Autoencoders.   (Reading University)
15:00 --- Coffee break ---
15:30
Seminars (until 17:00) (Reading University)
15:30 Seminar 7. PCA, Autoencoders (faces)   (Reading University)
17:00
Organisational (until 17:15) (Reading University)
17:15
Invited lectures (until 18:45) (Reading University)
19:00 --- Dinner ---
12:30
Organisational (until 13:30) (Reading University)
13:30
Lectures (until 16:00) (Reading University)
13:30 Lecture 9. RNNs.   (Reading University)
15:00 --- Coffee break ---
15:30 Certificates, Free Discussion   (Reading University)