curriculum
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curriculum [2020/08/08 14:44] scornet [MASTER 1] |
curriculum [2021/09/03 17:43] scornet [Scientific Courses, period 2 (January - March)] |
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| ==== Scientific courses, period 1 (October - December) ==== | ==== Scientific courses, period 1 (October - December) ==== | ||
| - | **MAP631 - Deep Learning (48h, 5 ECTS), Erwan Scornet (EP)** (contact: erwan.scornet@polytechnique.edu) | + | **MAP/INF631 - Deep Learning (48h, 5 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. | ||
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| During this course, the students will acquire the different methods underlying speech and language processing. The techniques and concepts that will be studied include: part-of-speech tagging, information extraction, knowledge representation, dependency parsing, and application of machine learning methods (such as deep learning, hidden markov models) to text classification. | During this course, the students will acquire the different methods underlying speech and language processing. The techniques and concepts that will be studied include: part-of-speech tagging, information extraction, knowledge representation, dependency parsing, and application of machine learning methods (such as deep learning, hidden markov models) to text classification. | ||
| - | **INF633 - Advanced 3D Graphics (24h, 2 ECTS), Marie-Paule Cani (EP), Julien Pettré (Inria), Pierre Ecormier (EP)** (contact: Marie-Paule.Cani@polytechnique.edu) | + | **INF633 - Advanced 3D Graphics: Exploring the links between Computer Graphics and AI (24h, 2 ECTS), Marie-Paule Cani (EP), Julien Pettré (Inria), Pierre Ecormier (EP)** (contact: Marie-Paule.Cani@polytechnique.edu) |
| - | Computer graphics tackles the creation of 3D contents, from object prototypes to animated scenes. This course will focus on the interactions between Computer Graphics and Artificial Intelligence, which recently lead to a number of advances. In particular, we will cover "Creative AI", ie. how interactive content creation can be enhanced using smart graphical models embedding knowledge, as well as the combination of 3D Graphics, AI and learning for the animation of virtual, autonomous creatures. | + | Computer graphics tackles the creation of 3D contents, from object prototypes to animated scenes. This course will focus on the interactions between Computer Graphics and Artificial Intelligence, which recently lead to a number of advances. In particular, we will cover "Creative AI", ie. how interactive content creation can be enhanced using smart graphical models embedding knowledge, as well as the combination of 3D Graphics, knowledge and learning for the animation and training of possibly autonomous, virtual creatures. |
| - | **INF634 - Computer Vision (24h, 2 ECTS), Vicky Kalogeiton (EP)** (contact: vicky.kalogeiton@gmail.com) | + | **INF634 - Advanced Computer Vision (24h, 2 ECTS), Vicky Kalogeiton (EP)** (contact: vicky.kalogeiton@polytehcnique.edu) |
| - | Being able to understand and reconstruct the world around us is essential for intelligent systems and robots. This course will detail computer vision techniques based on projective geometry - enabling to reconstructing a 3D world from pairs of images, introduce 3D reconstruction of shapes and motions from multiple cameras, and present new advances on object recognition in images and videos based on machine learning techniques. | + | This course is an introduction to fundamental and advanced topics in computer vision with learning-based approaches, ie. Deep Learning. Topics include image and video classification, object detection, action recognition, optical flow and motion, multi-modal vision systems, annotation signal and applications. |
| ==== Scientific Courses, period 2 (January - March) ==== | ==== Scientific Courses, period 2 (January - March) ==== | ||
| - | **MAP641 - Reinforcement Learning (48h, 5 ECTS), Odalric-Ambrym Maillard (Inria Lille), Bruno Scherrer (Inria Nancy), Olivier Pietquin (Google Brain) ** (contact: odalricambrym.maillard@inria.fr) | + | **MAP/INF641 - Reinforcement Learning (48h, 5 ECTS), Odalric-Ambrym Maillard (Inria Lille)** (contact: odalricambrym.maillard@inria.fr) |
| 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 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. | ||
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| ==== Transvere courses and projects (September to March) ==== | ==== Transvere courses and projects (September to March) ==== | ||
| - | **MIE630 - Seminar on ethical issues, law and novel applications of AI (3 ECTS):** Every Tuesday, 1:30pm-3:00pm | + | **MIE630 - Seminar on ethical issues, law and novel applications of AI (3 ECTS), Véronique Steyer <veronique.steyer@polytechnique.edu>, Louis Vuarin <louis.vuarin@polytechnique.edu>:** Every Tuesday, 1:30pm-3:00pm |
| - | Students will be sensitized to ethical issues and law, and introduced to novel application of artificial intelligence and visual computing through a weekly seminar with key-note talks from both institutional and industrial partners. The program of 2018-2019 seminars can be found **[[seminarProgramm|here]]** | + | Students will be sensitized to ethical issues and law, and introduced to novel application of artificial intelligence and visual computing through a weekly seminar with key-note talks from both institutional and industrial partners. The program of 2020-2021 seminars can be found **[[seminarProgramm|here]]** |
| **MAP/INF630 - Transverse project (3 ECTS):** Students will work half a day a week on a transverse project, corresponding to a challenging question either raised by an industrial partner or by a researcher in the domain spanned by the graduate degree. See the **[[transverse|transverse projects page]]** for details). | **MAP/INF630 - Transverse project (3 ECTS):** Students will work half a day a week on a transverse project, corresponding to a challenging question either raised by an industrial partner or by a researcher in the domain spanned by the graduate degree. See the **[[transverse|transverse projects page]]** for details). | ||
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| * MAP538: Refresher in statistics (two days, in September; Florian Bourgey <florian.bourgey@polytechnique.edu>) | * MAP538: Refresher in statistics (two days, in September; Florian Bourgey <florian.bourgey@polytechnique.edu>) | ||
| - | * INF538: Refresher in Computer science (two days, in September; Christophe Lino <christophe.lino@telecom-paris.fr>) | + | * INF538: Refresher in Computer science (two days, in September; Christophe Lino <christophe.lino@lix.polytechnique.fr>) |
curriculum.txt · Last modified: 2023/06/22 18:41 by scornet
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