MSc in Artificial Intelligence Academic o-Director Nikos Paragios Academic o-Director CĂŠline Hudelot Academic o-Director Vincent Mousseau
Artificial intelligence becomes a game changer of our life. The aim of this program is to provide the foundations and the most advanced techniques in the field towards becoming a technical leader of this transformation. Our program is unique in terms of curriculum since it encircles the field both with model/symbolic-driven and data-driven artificial intelligence methods as well as their applications to critical domains like natural language processing, visual computing, internet and retail. This unique end-to-end from theory to practice program entirely offered in English with outstanding quality of classes and instructors offers you a unique opportunity of excellence in terms of curriculum towards becoming an artificial intelligence architect and amazing career perspectives in the hottest discipline of this century.
• Foundations of Machine Learning: An overview of the most important trends in machine learning, with a particular focus on statistical risk and its minimization with respect to a prediction function is given in this course. A substantial lab section involves group projects on data science competitions and gives students the ability to apply the course theory to real-world problems. • Foundations of Artificial intelligence: An history and overview of the different approaches of Artificial Intelligence: from reflex agent (low level AI) to expert systems and xIA (high level AI). Each notion will be the subject of individual practical work. In addition, an AI will be developed by group and will compete in a tournament. • Foundations of Decision modeling: References are present and pervasive in many situations involving human interaction and decisions. References are expressed explicitly or implicitly in numerous applications and relevant decision should be made based on these preferences. This course aims at introducing preference models for multicriteria decisions. We will present concepts and methods for preference modelling and multicriteria decision making. • Foundations of Optimization: Foundational theory and methods for the solution of optimization problems iterative techniques for unconstrained minimization linear and nonlinear programming as well as discrete methods for engineering applications associated with programming exercises in Python are covered in this course. • Foundations of Deep Learning: This course will introduce the modern theory of convolutional neural networks, both in terms of theoretical concepts as well as in terms of practice with different training and programming architectures. Concrete examples on various applications domains will demonstrate the interest of these methods in artificial intelligence. • Foundations of Big Data & AI Programming Languages & Platforms : This course will teach you all about big data management - algorithms, techniques and tools needed to support big data processing with emphasis on the computational aspects related with programming of artificial intelligence methods based on machine learning.
• Ensemble Learning from Theory to Practice: The ensemble methods are popular machine learning techniques which are powerful when one wants to deal with both classification and regression prediction problems. The idea is to build a global prediction model by combining the strengths of a set of simpler base models. Random Forests approach is an example that fall into this category by the aggregation of a collection of trees. This course has the aim to study the key notions and concepts of tree-based methods and ensemble learning. We will introduce several methods, such as: Classification and Regression Trees (CART), boosting trees, Random Forests, etc. In addition, we want to investigate and discuss some recent challenges of interpretability and regularization through different approaches [Strobl et al., 2007, 2008]. Moreover, throughout the course the different approaches will be applied on practical cases involving real data. Particularly, we will discuss how to deal with uncertain data and missing values through a recent approach, namely uncertain trees. • Reinforcement learning: This course will introduce the foundations of dynamical problem modeling in artificial intelligence through reinforcement learning strategies. We will discuss optimization strategies, sampling strategies and rewards selection strategies at the concept and application level for various problems of artificial intelligence. • Excellence in Game Theory: This course will initially present the main principles concerning decision under uncertainty, and the use of graphical models when making decision under uncertainty Second, we will consider principles of game theory and show how such theory can model and analyze decision in situation where uncertain and strategic interactions are involved. • Inference and learning of Graphical Models: This course addresses mathematical foundations and computational solutions for training and optimizing (higher order) probabilistic graphical modes. These are powerful middle-level representations that once endowed with efficient optimization algorithms produce state of the art results for problems with average volume of training data. • Multi-agent Systems: The aim of this course is to study multi-agent systems, i.e. systems composed of multiple interacting computing elements, known as agents, as a paradigm for implementing autonomous and complex intelligent system. • Advanced Statistics: This course aims first at introducing the general methodology of mathematical statistics through the fundamental concepts (statistical modelling and sampling, estimation problems, decision theory and hypothesis testing). Then, this course provides advanced statistical techniques for multivariate analysis with a focus on computational statistics and robust estimation approaches. Regularized / penalized techniques are also presented. • Advanced Deep Learning: Deep learning methods are now the state of the art in many machine learning tasks, leading to impressive results. Nevertheless, they are still poorly understood, neural networks are still difficult to train, and the results are black boxes missing explanations. Given the societal impact of machine learning techniques today (used as assistance in medicine, hiring process, bank loans...), it is crucial to make their decisions explainable or to offer guarantees. Besides, real world problems usually do not fit the standard assumptions or frameworks of the most famous academic work (data quantity and quality, expert knowledge availability...). This course aims at providing insights and tools to address these practical aspects, based on mathematical concepts.
Applied AI: At least 3 electives to choose • Visual computing: This course will present an overview of trends, modern methods and applications of computer vision technologies in various problems of visual computing, namely visual analytics, object recognition, 3D scene modeling from multiple-views, cross training of multimodal data, etc. • Natural language processing: This course addresses fundamental questions at the intersection of human languages and computer science. In this course we explore methods inspired from symbolic and sub-symbolic artificial intelligence towards language understanding, parsing, translation & generation. • Networks science analytics: 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 in AI. The goal of this course is to present recent and state-of-the-art methods and algorithms for analyzing, mining and learning large-scale graph data, as well as their practical applications in various domains.
• Information retrieval and extraction: This course addresses the fundamentals of Information retrieval, the process of answering to an information need, expressed by a user’s query, by retrieving the relevant information in nonstructured data collections, often massive. This course will also cover recent approaches such that semantic web and question answering with knowledge graphs. A substantial practical section involves group projects on the design and building of a search application. • Deep learning for Medical Imaging: Medical imaging technologies provide unparalleled means to study structure and function of the human body in vivo. Interpretation of medical images is difficult due to the need to consider threedimensional, time-varying information from multiple types of medical images. Artificial intelligence (AI) holds great promises for assisting in the interpretation and medical imaging is one of the areas where AI is expected to lead to the most important successes. In the past years, deep learning technologies have led to impressive advances in medical image processing and interpretation. This course covers both theoretical and practical aspects of deep learning for medical imaging. It covers the main tasks involved in medical image analysis (classification, segmentation, registration, generative models…) for which state-of-the-art deep learning techniques are presented, alongside some more traditional image processing and machine learning approaches. Examples of different types of medical imaging applications (brain, cardiac…) will also be provided.
• You have graduated or will you soon graduate from a top university/school with a strong degree (Bachelor/M1) in engineering, mathematics, statistics, informatics, physics • You you have little to no work experience? • You have a good level of English and would you like to study entirely in English? • You looking to become an expert and a leader in AI?
Tuition fees in Paris: 20,000€ including 2,000€ deposit. You pay your deposit first, after acceptance into the program and before your registration. This amount is then later deducted from your tuition fee. Your remaining tuition fee can be paid each year in 2 equal installments in August & October.
Online Application dead lines • Round 1: Nov 20th, 2019 • Round 2: Jan 20th, 2020 • Round 3: March 5th, 2020 • Round 4: April 25th, 2020 • Round 5: June 30th, 2020
https://apply.centralesupelec.fr/
Academic Co-Directors Nikos Paragios CĂŠline Hudelot Vincent Mousseau
Administrative Director: Guillemette Breysse guillemette.breysse@centralesupelec.fr Program Coordinator: BĂŠatrice Billard beatrice.billard@centralesupelec.fr Program Development Manager: Adriano Cotta adriano.cotta@centralesupelec.fr
http://www.centralesupelec.fr/fr/msc-artificial-intelligence
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