With the support of

Artificial Intelligence

Broaden your knowledge in a rapidly advancing area of Science and Engineering and collaborate with peers from around the world! The summer school on Artificial Intelligence, hosted by CentraleSupélec, combines lectures, tutorials and hands-on sessions in such areas as machine learning, deep and reinforcement learning, computer vision and learning on networks. The programme also includes team-based competitions, along with networking opportunities with professionals of the research and industry sectors, and innovative cultural and social activities online.

Skills in Python and programming are required for the programme. If you are not familiar with Python, you will need to learn the basics prior to the start of the Summer School. In order to prove their level in Python, students will be asked to provide in the registration section a project previously completed demonstrating their programming skills.

Due to the ongoing pandemic and the travel restrictions that are being imposed, the Summer School on Artificial Intelligence will take place online this year.

Please visit our Tuition & Fees page for specific information.

Oearning outcomes

Objectives & Learning Outcomes

The Summer School in AI aims at introducing students to the field of Artificial Intelligence:

  •  by covering a wide range of topics, methodologies and related applications;
  • by giving students the opportunity to obtain hands-on experience on dealing with real-world problems;
  • with educational visits to companies and institutions where core AI research is taking place.

We expect that, by the end of the Summer School in AI, students will have:

  •  a good understanding of key topics and methodologies in machine learning and artificial intelligence in general, including deep learning, computer vision, reinforcement learning, natural language processing and network analysis;
  • strengthen their programming skills in Python via implementing and evaluating various machine learning algorithms and working on a real-world data challenge.

Broaden your skills

Boost your knowledge on Artificial Intelligence

Earn credit

Gain 3 credits and a certificate upon successful completion of the programme

Make friends

Work in teams with peers from all over the world

Programme description

Summer School programme description

Welcome Session

This session will be an opportunity to get to know each other, with team-building activities and ice-breaking games. You will be introduced to CentraleSupélec – Université Paris-Saclay and our international activities.

Introduction to Artificial Intelligence (Céline Hudelot)

This course will give an overview of the large field of Artificial Intelligence by first tracing its history and then by presenting the main Artificial Intelligence approaches: reflex-based, state-based, variable-based and logic-based. Students will apply these approaches during a practical lab session.

Introduction to Machine Learning (Richard Combes)

This course presents the fundamentals of statistical learning theory, from fundamental limits, to algorithms, their analysis and practical implementation. Important concepts such as Vapnik-Chervonenkis theory, Empirical Risk Minimization, Stochastic Gradient Descent algorithms and Kernel methods will be presented in details. A lab session will enable students to apply the theoretical contents to data.

Introduction to Deep Learning (Pablo Piantanida & Stergios Christodoulidis)

This course will provide students with the principles of representation learning and deep learning by covering the following subjects: Neural Networks, Backpropagation and stochastic gradient optimisation, Auto-encoders, Hyper-parameters and training tricks for neural networks, regularization, Deep Belief Networks and Deep Boltzmann Machines. Students will apply these approaches during a practical lab session.

Introduction to Reinforcement Learning (Mathieu Jonckheere)

Reinforcement learning algorithms permit to teach an agent to optimize how to get a reward. This can be helpful for solving games such as backgammon, but more recently it was applied to Atari games or the game of Go and has led to impressive results. This is thanks to a combination with deep learning techniques. During this class, students will review elementary properties of reinforcement learning. A lab session will be provided as well.

Machine Learning on Networks (Fragkiskos Malliaros)

Networks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. Characteristic examples include social networks (e.g., Facebook, Twitter), information networks (e.g., the Web) as well as technological networks (e.g., the Internet). The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and learning methods constitute prominent tools. The goal of this course is to present recent and state-of-the-art methods and algorithms for analyzing, mining and learning large-scale network data, as well as their practical applications in various domains (e.g., the web, social networks, recommender systems).

Introduction to Computer Vision (Hugues Talbot and Maria Vakalopoulou)

In this course students will survey the major aspects of Artificial Intelligence usage and techniques in the context of medical imaging. Medical imaging is a fascinating and major application area for Artificial Intelligence/Machine Learning where the stakes are high and the impact on society can be lasting and strong. However, this is also an application area fraught with challenges. In particular, is it important that AI results in this area be trustworthy, repeatable and auditable. Physicians are usually not happy with black-box decisions that come with no explanations. Fortunately many novel techniques exist to produce exactly this kind of results, which will be discussed in detail.

Introduction to Medical Image Analysis (Hugues Talbot and Maria Vakalopoulou)

Coming soon…

Artificial Intelligence System Architecture & Building Block (Anna Shillabeer)

The lecture will look into the notion of intelligence and compare this to what a computer does by using examples of expert systems especially in health and cybersecurity. Secondly, it will identify the components (e.g. machine learning, neural nets, analytics) of an ‘intelligent’ system (the high level architecture) and clarify what is the function of each. Thirdly, it will look into the strengths and weaknesses of intelligent systems in mission critical applications. Finally, it will try to assess what are the ethical and legal considerations of AI applications.

Learning from Limited Supervision: Models and Optimization (Ismail Ben Ayed)

Recently, learning from limited supervision has drawn tremendous research interests within the machine learning and computer vision communities. The purpose is to mitigate the lack of full and laborious annotations in a breadth of important application areas. In this talk, I will discuss some recent developments in this direction, highlighting and connecting various popular settings of learning from limited supervision: Few-shot learning, semi/weakly supervised learning and unsupervised domain adaptation. 

I will focus on how to tackle these problems by enforcing various types of regularizers, priors and constraints on training deep neural networks, which can leverage unlabeled data and embed domain-specific knowledge. I will discuss several key technical aspects in the context of learning with limited labels, including constrained optimization, Laplacian/Conditional Random Fields (CRFs) regularization and Shannon-Entropy/Mutual-Information losses. 

I will emphasize how more attention should be paid to optimization methods, going beyond standard gradient descent. The talk includes various experimental illustrations and applications. 

Artificial Intelligence and Ethics: a Review and Some Ethical Questions (Catherine Tessier)

The high number of documents as well as bodies created to deal with “the ethics of AI” leads us to wonder why AI has recently become a particular object of attention and which ethics is at stake. Starting from a review of international documents and recommendations, we will highlight issues with the vocabulary used, misleading postulates so as tensions and paradoxes. As an example, we will particularly focus on the “human control” principle.  We will then insist on the risks of misuse of ethics and the need for a true ethical reflection going with AI research so as with design and use of AI systems.

Screening Cardiac Disease in Underprivileged Environments (Miguel Tavares Coimbra)

According to the World Health Organization, cardiovascular diseases (CVDs) are in 2021 the number one cause of death globally, taking an estimated 17.9 million lives each year. Access to essential noncommunicable disease medicines and basic health technologies in all primary health care facilities is essential to ensure that those in need receive treatment and counselling. However, more than 75% of CVD deaths occur in low and middle income countries, where this access is not guaranteed, and trained clinicians are only available in small numbers. Portable technologies such as electronic stethoscopes and point-of-care ultrasound probes, when enhanced with artificial intelligence and computer assisted decision systems, could be the key to a more successful management of cardiac disease in underprivileged environments.

The schedule is subject to change. Please visit this page regularly for updates.

NB: Date and time is indicated according to Paris time zone, i.e. GMT+1.

Organizer

Organizer

Fragkiskos D. Malliaros

Fragkiskos D. Malliaros

Assistant Professor & Organizer

Academic Staff

Academic Staff

Ismail Ben Ayed

Ismail Ben Ayed

Associate Professor

Stergios Christodoulidis

Stergios Christodoulidis

Assistant Professor

Richard Combes

Richard Combes

Assistant Professor

Céline Hudelot

Céline Hudelot

Professor

Mathieu Jonckheere

Mathieu Jonckheere

Professor

Pablo Piantanida

Pablo Piantanida

Associate Professor

Anna Shillabeer

Anna Shillabeer

Professor

Hugues Talbot

Hugues Talbot

Professor

Catherine Tessier

Catherine Tessier

Senior Researcher

Maria Vakalopoulou

Maria Vakalopoulou

Assistant Professor

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