curriculum
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curriculum [2023/04/21 09:39] cani [Scientific Courses, period 2 (January - March)] |
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 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) | + | **INF649 - Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu) |
| - | Reinforcement learning (RL) is of increasing relevance today in science and industry, for developing autonomous agents in areas where dynamic decision making and adaptation is required; including games, energy systems, recommendation engines, finance, logistics, and auto-tuning the parameters of large and powerful computer models (for NLP, computer vision, etc). This course will take a deep look at the foundations of RL, the main learning paradigms (temporal-difference learning, Monte Carlo, and policy gradients methods), their modern state-of-the-art variants, and how they can be enhanced and deployed with deep-learning architectures to tackle problems at a scale found in the real world. We will also take a look at specialized topics such inverse reinforcement learning. | + | 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) | ||
| 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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