CERN School of Computing 2022

Classes per lecturer

• Francois Fluckiger
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    Special evening talk: When Internet history meets philosophy

• Andrei Gheata (CERN)
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    Software Design L2: Base Concepts of Parallel Programming: A Pragmatic Approach

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    Software Design L3: Understanding, Debugging and Profiling a Complex Multithreaded Application

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    Software Design - exercises

• Stephan Hageboeck (CERN)
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    Software Design L1: Parallelism in a Modern HEP Data Processing Framework

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    Software Design L4: Patterns for Parallel Software Development

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    Software Design - exercises

• Bob Jacobsen (UC Berkeley)
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    Data Science L1: Tools for interactive data exploration

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    Data Science L2: Interactive exploration of non-numeric data

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    Data Science - exercises

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    Tools and Techniques L1: Introduction

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    Tools and Techniques L2: Tools for Collaboration, Software Engineering Across the Project

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    Tools and Techniques - exercises

• Kamila Kalecinska (AGH)
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    Machine Learning - exercises

• Sebastian Lopienski (CERN)
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    Software Security L1: Introduction

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    Software Security L2: Security in different phases of software development

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    Software Security L3: Web application security, exercise debriefing

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    Software Security - exercises

• Eamonn Maguire (Proton)
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    Data Visualization L1: The Theory Behind Data Visualization

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    Data Visualization L2: Practical Applications of Theory and Multi-Dimensional Data Visualization

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    Data Visualization - exercises

• Alberto Pace (CERN)
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    Data Management L1: Setting the scene: Storage technologies, Storage reliability

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    Data Management L2: Cryptography, authentication, authorization and accounting 1

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    Data Management L3: Cryptography, authentication, authorization and accounting 2

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    Data Management L4: Distributed Hash Tables, Data Replication, Caching, Monitoring, Alarms and Quota 1

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    Data Management L5: Distributed Hash Tables, Data Replication, Caching, Monitoring, Alarms and Quota 2

• Andreas Joachim Peters (CERN)
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    Data Technologies - exercises

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    Data Technologies: Introduction

• Arnulf Quadt (University of Göttingen)
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    Introduction to Physics Computing L1: Hadron Collider Physics

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    Introduction to Physics Computing L2: Digital Data, Simulation and Reconstruction in Modern Particle Physics Experiments

• Toni Sculac (University of Split)
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    Data Analysis L1: Introduction

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    Data Analysis L2: Probability density functions and Monte Carlo methods

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    Data Analysis L3: Parameter estimation and confidence intervals

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    Data Analysis L4: Hypothesis testing and p-value

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    Data Analysis - exercises

• Tomasz Szumlak (AGH)
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    (optional) CUDA training

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    Guest lecture: Heterogeneous computing

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    Machine Learning L1

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    Machine Learning L2

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    Machine Learning L3

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    Machine Learning - exercises