Thematic CERN School of Computing on Machine Learning 2024

13 Oct 2024, 15:00 → 19 Oct 2024, 11:00 Europe/Zurich

MedILS, Split, Croatia

Alberto Pace (CERN), Andrzej Nowicki (CERN), Kristina Gunne (CERN)

The 15th Thematic CERN School of Computing (tCSC Machine Learning 2024) will take place on October 13-19, 2024. 

The school will focus on the theme of Machine Learning and Artificial Intelligence applied to Data Analysis and Accelerator Technology. The programme will offer 22 hours of lectures and hands-on exercises, and student presentation sessions.

This school is organized by CERN in collaboration with the Faculty of Science, University of Split. The school will take place in Split, Croatia, and be hosted at the Mediterranean Institute For Life Sciences (MEDILS) Conference Centre. The Centre is a historical renovated building situated in a wooded and landscaped park located on the Adriatic Sea coast, a few kilometers from the centre of Split.

Important dates 2024

• 8 May application opens
• 19 June application close
• 3 July invitations sent to selected students
• 4 September participation fee deadline

 

Final School Movie

Photo Gallery

School booklet

Sunday 13 October

1 Registration at MedILS

2 Transport to Split

3 Guided tour of Split

19:30 Welcome dinner at Restoran Para di šoto

Monday 14 October

4 opening session

Speaker: Alberto Pace (CERN)

2024-10-14-School-Presentation-ML-Split-v03-Monday.pdf

2024-10-14-School-Presentation-ML-Split-v03-Monday.pptx

5 Machine learning methods: L1 Introduction to Statistics

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.

Speaker: Toni Sculac (University of Split Faculty of Science (HR))

Lecture_1_tCSC2024.pdf

6 Announcements

tCSC School logistics and updates.pdf

tCSC School logistics and updates.pptx

11:00 Coffee

7 Machine learning methods: L2 Statistics and Machine Learning

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.

Speaker: Toni Sculac (University of Split Faculty of Science (HR))

Lecture_2_tCSC2024.pdf

12:30 Lunch

8 Study time or daily sports

15:15 Coffee

9 Machine learning methods: L3 Classical Machine Learning

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.

Speaker: Toni Sculac (University of Split Faculty of Science (HR))

Lecture_3_tCSC2024.pdf

16:45 Break

10 Machine Learning methods: excercise 1

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

dataA.csv

solutions1.ipynb

tCSC_MachineLearning_Exercise_Instructions.pdf

tCSC_ml_set_1.pdf

tCSC solution1.pdf

11 Machine Learning methods: excercise 2

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

dataA.csv

dataB.csv

solutions2.ipynb

tCSC_MachineLearning_Exercise_Instructions.pdf

tCSC_ml_set_2-1.pdf

19:30 Dinner at MedILS

Tuesday 15 October

12 Machine Learning in Accelerator Technologies: Machine Learning for particle accelerators

Main use cases and applications

Speaker: Verena Kain (CERN)

Lecture_1_vkain_intro.pdf

13 Machine Learning in Accelerator Technologies: Bayesian Optimisation

Speaker: Verena Kain (CERN)

Lecture_2_vkain_BO.pdf

14 Announcements

11:00 Coffee

15 Machine Learning Methods: L4 Introduction to Deep Learning

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.

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

DeepNN.ipynb

Lecture_4_tCSC2024.pdf

12:30 Lunch

16 Study time or daily sports

15:15 Coffee

17 Machine Learning Methods: L5 Advanced Deep Learning

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

Link to slides

Link to slides

ml CSC .pdf

16:45 Break

18 Machine learning in accelerators: Exercise 1

Speakers: Michael Schenk (CERN), Verena Kain (CERN)

Ex1.ipynb

Ex2.ipynb

Ex3.ipynb

Exercises_ML4acc.pdf

19 Machine learning in accelerators: Exercise 2

Speakers: Michael Schenk (CERN), Verena Kain (CERN)

Exercises_ML4acc.pdf

19:30 Dinner at MedILS

Wednesday 16 October

20 Machine Learning in Accelerators: Introduction to Reinforcement Learning

Speaker: Michael Schenk (CERN)

rl_intro_161024.pdf

rl_intro_161024.pptx

21 Machine Learning in Accelerators: Advanced concepts for Reinforcement Learning

Speaker: Verena Kain (CERN)

Lecture_4_vkain_advancedRL.pdf

22 Announcements

11:00 Coffee

23 Machine Learning methods: exercise 3

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

dataA.csv

dataB.csv

dataC.csv

solutions3a.ipynb

solutions3b.ipynb

tCSC_MachineLearning_Exercise_Instructions.pdf

tCSC_ml_set_3a.pdf

tCSC_ml_set_3b.pdf

12:15 Lunch

24 River rafting excursion

Departure from MedILS at 13h

18:30 Dinner at Kastel Slanica Omis

25 Transport back to medILS

Thursday 17 October

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

Speaker: Dr Sofia Vallecorsa (CERN)

CSC24_Lecture1_Intro_Architectures.pdf

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

Speaker: Dr Sofia Vallecorsa (CERN)

CSC24_Lecture2_use_cases.pdf

28 Announcements

29 Group photo

11:05 Coffee

30 Machine learning in Data Analysis: Generative Models for HEP

Speaker: Ilaria Luise (CERN)

CSC24_Lecture3_foundation_models.pdf

12:30 Lunch

31 Study time or daily sports

15:15 Coffee

32 Machine learning in accelerators: Exercise 3

Speaker: Verena Kain (CERN)

Ex4.ipynb

Ex5.ipynb

rl_awake_ex.pdf

rl_awake_ex.pptx

16:45 Break

33 Machine learning in Data Analysis: Exercise 1

Speakers: Ilaria Luise (CERN), Dr Sofia Vallecorsa (CERN)

CSC24_exercises.pdf

link to dl4phys

34 Machine learning in Data Analysis: Exercise 2

Speakers: Ilaria Luise (CERN), Dr Sofia Vallecorsa (CERN)

19:30 Dinner

Friday 18 October

35 Lightning talks

01_AndreaPaccagnela.pdf

02_YanxiGu.pdf

03_AlbertoPimpo.pdf

04_JoelWulff.pdf

04_JoelWulff.pptx

06_VeronicaKirsebom.pdf

08_Luka

09_NikolinaBunijevac.pdf

09_NikolinaBunijevac.pptx

36 Machine learning in Data Analysis: Systematics in ML

Speaker: Ilaria Luise (CERN)

CSC24_Lecture4_Systematics_ML.pdf

37 Announcements

11:00 Coffee

38 Machine learning in Data Analysis: Exercise 3

Speakers: Ilaria Luise (CERN), Dr Sofia Vallecorsa (CERN)

CSC24_Lecture5_Extra_Material.pdf

12:30 Lunch

39 Exam

14:30 Break

40 Closing ceremony

Speaker: Alberto Pace (CERN)

41 Sports and leisure time

19:30 Closing dinner in Restaurant Kavanazona

Saturday 19 October

42 Departures from MedILS