Registration at Hotel Comfort Hotel Malmö Carlsgatan 10 C, 211 20 Malmö, Sweden Location: https://maps.app.goo.gl/fayc5oVwx9WtBgqJ8

Walk around Malmö

opening session

• Alberto Pace (CERN)

Machine learning methods: L1 Introduction to Statistics

• Toni Sculac (University of Split Faculty of Science (HR))

In this lecture we will go over key concepts in statistics which are the cornerstone of mathematical foundation of Machine Learning. We will define frequentistic and Bayesian probabilities, learn what is a PDF. We will also discuss parameter estimation with the Maximum Likelihood method and finish with the definition of Confidence Intervals.

Announcements

Machine learning methods: L2 Statistics and Machine Learning

• Toni Sculac (University of Split Faculty of Science (HR))

We start this lecture with unfolding and hypothesis testing, another two key concepts from statistics. Key part of the lecture is the Neyman-Person lemma that paves a clear path for the needs of Machine Learning in statistics.

Machine learning methods: L3 Classical Machine Learning

• Toni Sculac (University of Split Faculty of Science (HR))

We continue tackling the problem of trying to know the likelihood ratio with the use of Classical Machine Learning. We try to solve it by brute force and then we move to Machine Learning techniques. We start with a Kernel Density Estimators. We continue by defining what is a decision tree, what is a leaf and we study how it works on a very simple example. We go further and explain the difference between classification and regression, as well as the need for pruning, bagging, and boosting. This main goal of this lecture is to remove the idea of the “black-box approach" and understand all of the details of a decision tree.

Machine Learning methods: excercise 1

• Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
• Toni Sculac (University of Split Faculty of Science (HR))

Machine Learning methods: excercise 2

• Toni Sculac (University of Split Faculty of Science (HR))
• Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))

Study time or daily sports

Machine Learning in Accelerator Technologies: Machine Learning for particle accelerators

• Verena Kain (CERN)

Main use cases and applications

Machine Learning in Accelerator Technologies: Bayesian Optimisation

• Verena Kain (CERN)

Announcements

Machine Learning Methods: L4 Introduction to Deep Learning

• Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
• Toni Sculac (University of Split Faculty of Science (HR))

We introduce the concept of a Neural Network (NN) and study their application with a single-neuron network. This again allows us to avoid the "black-box approach" and really understand the key concepts of how a NN works. We discuss activation functions and how the NN learns with the help of the loss functions and backpropagation. We finish by discussing the basic idea of a Deep Neural Network and basic training concepts.

Machine Learning Methods: L5 Advanced Deep Learning

• Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
• Toni Sculac (University of Split Faculty of Science (HR))

Machine learning in accelerators: Exercise 1

• Michael Schenk (CERN)
• Verena Kain (CERN)

Machine learning in accelerators: Exercise 2

• Verena Kain (CERN)
• Michael Schenk (CERN)

Study time or daily sports

Machine Learning in Accelerators: Introduction to Reinforcement Learning

• Michael Schenk (CERN)

Machine Learning in Accelerators: Advanced concepts for Reinforcement Learning

• Verena Kain (CERN)

Announcements

Machine Learning methods: exercise 3

• Toni Sculac (University of Split Faculty of Science (HR))
• Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))

Afternoon activity

Machine learning in Data Analysis: Introduction to Machine Learning for HEP, Anomaly detection and real time applications

• Sofia Vallecorsa (CERN)

Machine learning in Data Analysis: The data reconstruction step - a pattern recognition problem

• Sofia Vallecorsa (CERN)

Announcements

Group photo

Machine learning in Data Analysis: Generative Models for HEP

• Ilaria Luise (CERN)

Machine learning in Data Analysis: Exercise 1

• Sofia Vallecorsa (CERN)
• Ilaria Luise (CERN)

Machine learning in Data Analysis: Exercise 2

• Ilaria Luise (CERN)
• Sofia Vallecorsa (CERN)

Machine learning in accelerators: Exercise 3

• Verena Kain (CERN)

Study time or daily sports

Lightning talks

Machine learning in Data Analysis: Systematics in ML

• Ilaria Luise (CERN)

Announcements

Machine learning in Data Analysis: Exercise 3

• Ilaria Luise (CERN)
• Sofia Vallecorsa (CERN)

Exam

Closing ceremony

• Alberto Pace (CERN)

Sports and leisure time

Departures