A word from the IT Department Head

• Frederic Hemmer (CERN)

Room: 31/3-004 - IT Amphitheatre

Introduction to the inverted CSC

• Sebastian Lopienski (CERN)

Room: 31/3-004 - IT Amphitheatre

Let your machine do the learning - Lecture 1

• Daniel Hugo Campora Perez (CERN & Universidad de Sevilla)

Room: 31/3-004 - IT Amphitheatre The field of Artificial Intelligence, whose formal definitions go as back as the 40's, have recently gained a renowned interest in the community as more and more problems become amenable to be tackled by it. Even better, we are now available to try complex techniques in a very accessible manner, lowering the entrance admission to this cool club to just a couple of hours. In this series of lectures, we are going to explore non-conventional techniques to solve long standing problems, coming from AI, from a pragmatic and up to date perspective. By the end of these lectures you should be able to get your hands dirty with exciting real examples. You should be able to identify what kind of problems are you dealing with, what tools does AI have in store for you and how to apply them in a straightforward way, with room for depth. Just enjoy longer coffee breaks as the machine works it out for you.

Algorithms for Anomaly Detection - Lecture 1

• Michael Davis (CERN)

Room: 31/3-004 - IT Amphitheatre The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detection research.

Applying natural evolution for solving computational problems - Lecture 1

• Daniel Lanza Garcia (CERN, Switzerland)

Room: 31/3-004 - IT Amphitheatre Darwin’s natural evolution theory has inspired computer scientists for solving computational problems. In a similar way to how humans and animals have evolved along millions of years, computational problems can be solved by evolving a population of solutions through generations until a good solution is found. In the first lecture, the fundaments of evolutionary computing (EC) will be described, covering the different phases that the evolutionary process implies. ECJ, a framework for researching in such field, will be also explained. In the second lecture, genetic programming (GP) will be covered. GP is a sub-field of EC where solutions are actual computational programs represented by trees. Bloat control and distributed evaluation will be introduced.

Distributed consensus and fault tolerance - Lecture 1

• Georgios Bitzes (CERN)

Room: 31/3-004 - IT Amphitheatre In a world where clusters with thousands of nodes are becoming commonplace, we are often faced with the task of having them coordinate and share state. As the number of machines goes up, so does the probability that something goes wrong: a node could temporarily lose connectivity, crash because of some race condition, or have its hard drive fail. What are the challenges when designing fault-tolerant distributed systems, where a cluster is able to survive the loss of individual nodes? In this lecture, we will discuss some basics on this topic (consistency models, CAP theorem, failure modes, byzantine faults), detail the raft consensus algorithm, and showcase an interesting example of a highly resilient distributed system, bitcoin.

Algorithms for Anomaly Detection - Lecture 2

• Michael Davis (CERN)

Room: 31/3-004 - IT Amphitheatre The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detection research.

Virtual Machine Images Management in Cloud Environments

• Lorena Lobato Pardavila (CERN & Universidad de Oviedo)

Room: 31/3-004 - IT Amphitheatre Nowadays, the demand for scalability in distributed systems has led a design philosophy in which virtual resources need to be configured in a flexible way to provide services to a large number of users. The configuration and management of such an architecture is challenging (e.g.: 100,000 compute cores on the private cloud together with thousands of cores on external cloud resources). There is the need to process CPU intensive work whilst ensuring that the resources are shared fairly between different users of the system, and guarantee that all nodes are up to date with new images containing the latest software configurations. Different types of automated systems can be used to facilitate the orchestration. CERN’s current system, composed of different technologies such as OpenStack, Packer, Puppet, Rundeck and Docker will be introduced and explained, together with the process used to create new Virtual Machines images at CERN.

Let your machine do the learning - Lecture 2

• Daniel Hugo Campora Perez (CERN & Universidad de Sevilla)

Room: 31/3-004 - IT Amphitheatre The field of Artificial Intelligence, whose formal definitions go as back as the 40's, have recently gained a renowned interest in the community as more and more problems become amenable to be tackled by it. Even better, we are now available to try complex techniques in a very accessible manner, lowering the entrance admission to this cool club to just a couple of hours. In this series of lectures, we are going to explore non-conventional techniques to solve long standing problems, coming from AI, from a pragmatic and up to date perspective. By the end of these lectures you should be able to get your hands dirty with exciting real examples. You should be able to identify what kind of problems are you dealing with, what tools does AI have in store for you and how to apply them in a straightforward way, with room for depth. Just enjoy longer coffee breaks as the machine works it out for you.

Creating Effective Data Visualizations - Lecture 1

• Eamonn Maguire (Pictet Asset Management)

Room: 31/3-004 - IT Amphitheatre In this course I aim to give an overview of data visualisation as a field, including many of the important theoretical groundings in data visualization. We will explore the different ways of representing visual information, and the strengths/weaknesses of those approaches. Using real-world case studies, I will demonstrate techniques and best practices for visualizing complex multi-dimensional data common to high energy physics and other fields.

Distributed consensus and fault tolerance - Lecture 2

• Georgios Bitzes (CERN)

Room: 31/3-004 - IT Amphitheatre In a world where clusters with thousands of nodes are becoming commonplace, we are often faced with the task of having them coordinate and share state. As the number of machines goes up, so does the probability that something goes wrong: a node could temporarily lose connectivity, crash because of some race condition, or have its hard drive fail. What are the challenges when designing fault-tolerant distributed systems, where a cluster is able to survive the loss of individual nodes? In this lecture, we will discuss some basics on this topic (consistency models, CAP theorem, failure modes, byzantine faults), detail the raft consensus algorithm, and showcase an interesting example of a highly resilient distributed system, bitcoin.

Applying natural evolution for solving computational problems - Lecture 2

• Daniel Lanza Garcia (CERN, Switzerland)

Room: 31/3-004 - IT Amphitheatre Darwin’s natural evolution theory has inspired computer scientists for solving computational problems. In a similar way to how humans and animals have evolved along millions of years, computational problems can be solved by evolving a population of solutions through generations until a good solution is found. In the first lecture, the fundaments of evolutionary computing (EC) will be described, covering the different phases that the evolutionary process implies. ECJ, a framework for researching in such field, will be also explained. In the second lecture, genetic programming (GP) will be covered. GP is a sub-field of EC where solutions are actual computational programs represented by trees. Bloat control and distributed evaluation will be introduced.

Creating Effective Data Visualizations - Lecture 2

• Eamonn Maguire (Pictet Asset Management)

Room: 31/3-004 - IT Amphitheatre In this course I aim to give an overview of data visualisation as a field, including many of the important theoretical groundings in data visualization. We will explore the different ways of representing visual information, and the strengths/weaknesses of those approaches. Using real-world case studies, I will demonstrate techniques and best practices for visualizing complex multi-dimensional data common to high energy physics and other fields.

Let your machine do the learning - hands-on session

• Daniel Hugo Campora Perez (CERN & Universidad de Sevilla)

Room: 31/3-004 - IT Amphitheatre

Closing remarks

• Sebastian Lopienski (CERN)

Room: 31/3-004 - IT Amphitheatre