curriculum
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curriculum [2022/10/04 17:25] cani [Scientific courses, period 1 (October - December)] |
curriculum [2023/06/22 18:40] scornet [Scientific Courses, period 2 (January - March)] |
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| ==== Detailed curriculum ==== | ==== Detailed curriculum ==== | ||
| + | ===== MASTER 1 ===== | ||
| + | All courses are 36h and will represent 4 ECTS. | ||
| + | |||
| + | * MAP538: Refresher in statistics (two days, in September; Maxime Sangnier <maxime.sangnier@lpsm.paris>) | ||
| + | * INF538: Refresher in Computer science (two days, in September; XXXX Ask Amal Dev Parrakat?) | ||
| + | |||
| + | === Period 1 === | ||
| + | |||
| + | + Scientific courses: | ||
| + | |||
| + | Mandatory: | ||
| + | * Digital representation and analysis of shapes (INF574, Mathieu Desbrun, Pooran Memari, EP & Inria) | ||
| + | |||
| + | 1 course among: | ||
| + | * Machine Learning I (INF554, Michalis Vazirgiannis, EP) | ||
| + | * Foundations of Machine Learning (MAP553, Erwan Le Pennec, EP) | ||
| + | |||
| + | 2 courses among: | ||
| + | * Image Analysis and Computer Vision (INF573, Mathieu Brédif, EP & IGN) | ||
| + | * Constraint-based Modeling and Algorithms for Decision Making Problems (INF555, François Fages, EP & Inria) | ||
| + | * Signal processing (MAP555, Rémi Flamary, EP) | ||
| + | * Topological data analysis (INF556, Steve Oudot, EP & Inria) | ||
| + | |||
| + | + Mandatory non-scientific courses | ||
| + | |||
| + | * Fundamental of Strategy and Innovation (MIE555) or Marketing and Strategy Introduction (MIE556, Workload ++) | ||
| + | * Sport | ||
| + | * Humanities | ||
| + | * Foreign languages | ||
| + | |||
| + | |||
| + | === Period 2 === | ||
| + | |||
| + | Mandatory course: | ||
| + | * Advanced Machine Learning and autonomous agents (INF581, Jesse Read, EP) | ||
| + | * Computer animation (INF585, Damien Rohmer, EP) | ||
| + | |||
| + | 2 scientific courses among | ||
| + | * Regression (MAP569, Karim Lounici, EP, difficult math course, with pre-requisit in mathematical fundations of ML, see also the book (2018) Foundations of Machine Learning; please contact the teacher before the beginning of the course) | ||
| + | * Statistics in action (MAP566, Julien Chiquet, EP & Agro Paris Tech) | ||
| + | * Real-time AI in Video Games: decisive & collaborative actions (INF584A, David Bilemdjian, Chaire Ubisoft) | ||
| + | * Algorithmic geometry: from theory to applications (INF562, Luca Castelli, EP) | ||
| + | * Image synthesis: Theory and practice (INF584, Tamy Boubekeur, Telecom ParisTech) | ||
| + | |||
| + | + Mandatory non-scientific courses | ||
| + | * Entrepreneurship for sustainability (MIE568, wednesday morning), or Managing sustainable innovation (MIE565, friday afternoon) | ||
| + | * Sport | ||
| + | * Humanities | ||
| + | * Foreign languages | ||
| + | |||
| + | === Period 3 === | ||
| + | |||
| + | MAP/INF590 - Internship (4 to 6 months) | ||
| ===== MASTER 2 ====== | ===== MASTER 2 ====== | ||
| ==== Pre-training period (September) ==== | ==== Pre-training period (September) ==== | ||
| Choice between: | Choice between: | ||
| * MAP630 - Refresher in Statistics : statistical analysis, introduction to Machine Learning techniques (Pierre Latouche, CNRS). | * MAP630 - Refresher in Statistics : statistical analysis, introduction to Machine Learning techniques (Pierre Latouche, CNRS). | ||
| - | * INF630 - Refresher in Computer Science : C++ programming, basics of 3D modeling, algorithmic geometry and computer animation (Pooran Memari, CNRS and Damien Rohmer, EP). | + | * INF630 - Refresher in Computer Science : Algorithmic geometry and character animation (Pooran Memari, CNRS and Marie-Paule Cani, EP). |
| ==== Scientific courses, period 1 (October - December) ==== | ==== Scientific courses, period 1 (October - December) ==== | ||
| - | **MAP/INF631 - Deep Learning (48h, 5 ECTS), Erwan Scornet (EP)** (contact: erwan.scornet@polytechnique.edu) | + | **MAP/INF631 - Deep Learning (48h, 4 ECTS), Erwan Scornet (EP)** (contact: erwan.scornet@polytechnique.edu) |
| Deep Learning is one key element of modern data science. This course will explore several instances of Deep Neural Networks, each one being specifically adapted to solve a particular learning task (classification, image recognition, text mining, dimensionality reduction). An introduction to current research topics on neural network will be presented during the last part of the course. | Deep Learning is one key element of modern data science. This course will explore several instances of Deep Neural Networks, each one being specifically adapted to solve a particular learning task (classification, image recognition, text mining, dimensionality reduction). An introduction to current research topics on neural network will be presented during the last part of the course. | ||
| Line 30: | Line 83: | ||
| ==== Scientific Courses, period 2 (January - March) ==== | ==== Scientific Courses, period 2 (January - March) ==== | ||
| - | **MAP/INF641 - Reinforcement Learning (48h, 5 ECTS), Odalric-Ambrym Maillard (Inria Lille)** (contact: odalricambrym.maillard@inria.fr) | + | **INF649 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) |
| - | Reinforcement learning aims at finding at each step of a process the best action to take in order to minimize some regret function. This course will introduce the general notions of reinforcement learning and will present several online algorithms that can be used in real-time to take actions. The specificity and the performance of the different algorithms will be discussed in detail. | + | Reinforcement learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, and for auto-tuning the parameters of other learning frameworks. This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods). We will explore them further, and study modern state-of-the-art variants (such as proximal policy optimization), with a focus on developing RL solutions with deep neural architectures suited to modern applications. We will also take a look at specialized topics such inverse reinforcement learning. |
| **INF641 - Introduction to the verification of neural networks (24h, 2 ECTS), Eric Goubault (EP), Sylvie Putot (EP)** (contact: sylvie.putot@polytechnique.edu) | **INF641 - Introduction to the verification of neural networks (24h, 2 ECTS), Eric Goubault (EP), Sylvie Putot (EP)** (contact: sylvie.putot@polytechnique.edu) | ||
| Neural networks are widely used in numerous applications including safety-critical ones such as control and planning for autonomous systems. A central question is how to verify that they are correct with respect to some specification. Beyond correctness or robustness, we are also interested in questions such as explainability and fairness, that can in turn be specified as formal verification problems. In this course, we will see how formal methods approaches introduced in the context of program verification can be leveraged to address the verification of neural networks. | Neural networks are widely used in numerous applications including safety-critical ones such as control and planning for autonomous systems. A central question is how to verify that they are correct with respect to some specification. Beyond correctness or robustness, we are also interested in questions such as explainability and fairness, that can in turn be specified as formal verification problems. In this course, we will see how formal methods approaches introduced in the context of program verification can be leveraged to address the verification of neural networks. | ||
| - | Or, instead of INF641, **INF657G - Navigation for Autonomous systems (24h, 2 ECTS), David Filliat (ENSTA)** (contact: david.filliat@ensta-paris.fr) | + | **INF657G - Navigation for Autonomous systems (24h, 2 ECTS), David Filliat (ENSTA)** (contact: david.filliat@ensta-paris.fr) |
| Drones and robots must create maps of their surroundings to plan their movement and navigate. This course presents the robotic platforms and the most common sensors (vision, Lidar, intertial units, odometry …) and the different components of navigation: control; obstacle avoidance; localization; mapping (SLAM) and trajectory planning as well as filtering (Kalman filter, particle filtering, etc.) and optimization techniques used in these fields. | Drones and robots must create maps of their surroundings to plan their movement and navigate. This course presents the robotic platforms and the most common sensors (vision, Lidar, intertial units, odometry …) and the different components of navigation: control; obstacle avoidance; localization; mapping (SLAM) and trajectory planning as well as filtering (Kalman filter, particle filtering, etc.) and optimization techniques used in these fields. | ||
| Line 59: | Line 112: | ||
| - | ==== Final project (April to September - 30 ECTS) ==== | + | ==== Final project (April to September - 32 ECTS) ==== |
| MAP/INF690 - Internship: 5 to 6 months project, either in the R&D department of a company or in a research lab. | MAP/INF690 - Internship: 5 to 6 months project, either in the R&D department of a company or in a research lab. | ||
| - | ===== MASTER 1 ===== | ||
| - | |||
| - | All courses are 36h and will represent 4 ECTS. | ||
| - | |||
| - | * MAP538: Refresher in statistics (two days, in September; Maxime Sangnier <maxime.sangnier@lpsm.paris>) | ||
| - | * INF538: Refresher in Computer science (two days, in September; XXXX Ask Amal Dev Parrakat?) | ||
| - | |||
| - | === Period 1 === | ||
| - | |||
| - | + Scientific courses: | ||
| - | |||
| - | Mandatory: | ||
| - | * Digital representation and analysis of shapes (INF574, Mathieu Desbrun, Pooran Memari, EP & Inria) | ||
| - | 1 course among: | ||
| - | * Machine Learning I (INF554, Michalis Vazirgiannis, EP) | ||
| - | * Foundations of Machine Learning (MAP553, Erwan Le Pennec, EP) | ||
| - | |||
| - | 2 courses among: | ||
| - | * Image Analysis and Computer Vision (INF573, Mathieu Brédif, EP & IGN) | ||
| - | * Constraint-based Modeling and Algorithms for Decision Making Problems (INF555, François Fages, EP & Inria) | ||
| - | * Signal processing (MAP555, Rémi Flamary, EP) | ||
| - | * Topological data analysis (INF556, Steve Oudot, EP & Inria) | ||
| - | |||
| - | + Mandatory non-scientific courses | ||
| - | |||
| - | * Fundamental of Strategy and Innovation (MIE555) or Marketing and Strategy Introduction (MIE556, Workload ++) | ||
| - | * Sport | ||
| - | * Humanities | ||
| - | * Foreign languages | ||
| - | |||
| - | |||
| - | === Period 2 === | ||
| - | |||
| - | Mandatory course: | ||
| - | * Advanced Machine Learning and autonomous agents (INF581, Jesse Read, EP) | ||
| - | * Computer animation (INF585, Damien Rohmer, EP) | ||
| - | |||
| - | 2 scientific courses among | ||
| - | * Regression (MAP569, Karim Lounici, EP, difficult math course, with pre-requisit in mathematical fundations of ML, see also the book (2018) Foundations of Machine Learning; please contact the teacher before the beginning of the course) | ||
| - | * Statistics in action (MAP566, Julien Chiquet, EP & Agro Paris Tech) | ||
| - | * Real-time AI in Video Games: decisive & collaborative actions (INF584A, David Bilemdjian, Chaire Ubisoft) | ||
| - | * Algorithmic geometry: from theory to applications (INF562, Luca Castelli, EP) | ||
| - | * Image synthesis: Theory and practice (INF584, Tamy Boubekeur, Telecom ParisTech) | ||
| - | |||
| - | + Mandatory non-scientific courses | ||
| - | * Entrepreneurship for sustainability (MIE568, wednesday morning), or Managing sustainable innovation (MIE565, friday afternoon) | ||
| - | * Sport | ||
| - | * Humanities | ||
| - | * Foreign languages | ||
| - | |||
| - | === Period 3 === | ||
| - | |||
| - | MAP/INF590 - Internship (4 to 6 months) | ||
curriculum.txt · Last modified: 2023/06/22 18:41 by scornet
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