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==== Graduate Degree in Artificial Intelligence and Advanced Visual Computing:====
==== Detailed curriculum ====


===== MASTER 2 ======
==== Pre-training period (September) ==== 
Choice between:
  * 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).


==== Scientific courses, period 1 (October - December) ====

**MAP631 - 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. 

**INF631 - Data Analysis : geometry and topology in arbitrary dimensions (24h, 2 ECTS), Steve Oudot (EP) ** (contact: steve.oudot@inria.fr)
  This course will cover the use of geometric and topological methods for analyzing data in arbitrary dimensions, including the efficient computation of proximity queries, Delaunay based reconstruction, a variety of clustering methods (agglomerative, hierarchical, mean-shift), and the use of topological descriptors for interpreting and processing high dimensional data.
   
**INF632 - Natural Language and speech Processing : from knowledge modeling to machine learning (24h, 2 ECTS), Chloé Clavel (Telecom ParisTech), Fabian Suchanek (Telecom ParisTech) ** (contact: suchanek@telecom-paristech.fr)
  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)
  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. 
   
**INF634 - Computer Vision  (24h, 2 ECTS), Renaud Keriven (EP & Bentley systems)** (contact: Renaud.Keriven@bentley.com)
  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.



==== 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)
   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. 

**INF641 - Robot motion planning, verification and control of hybrid systems (24h, 2 ECTS), David Filliat (ENSTA), Eric Goubault (EP), Sylvie Putot (EP)** (contact: david.filliat@ensta-paristech.fr)
  Drones and robots need to build maps of their environments to plan their motion and navigate. Moreover, enforcing rules and verifying that these moving entities stick to their specifications is essential for safety. This course will focus on safe robot navigation, introduce map building techniques, present motion planning methods and give an introduction to control and verification of the resulting hybrid systems.

**INF642 - Socio-emotional embodied conversational agents (24h, 2 ECTS), Catherine Pelachaud (CNRS - ISIR), Chloé Clavel (TelecomParistech),  Frederic Landragin (CNRS), Michael Neff (University of California, Davis)** (contact: catherine.pelachaud@upmc.fr)
  Many interactive systems, from virtual companions to online retailing, rely on embodied conversational agents. These agents need to reach a good level of communication skills to conduct a conversation with humans and be acceptable and trustworthy by humans. This course will introduce non-verbal behavior models, present models for multimodal dialog, opinion detection and voice quality, explain how to model the agent's emotions and their evolution over time, and present methods for enhancing naturalism with expressive gaze and gestures, realistic animation. 


**INF643 - Soft robots: simulation, fabrication, and control (24h, 2 ECTS),  Christian Duriez (Inria Lille), Sylvain Lefebvre (Inria Nancy) ** (contact: christian.duriez@inria.fr)
  Soft robotics is a promising novel field, bringing more robustness in robots design and for all tasks involving close interactions with humans, from help to disable people to medical robot. This course will give an introduction to recent advances in soft robotics, including topological optimization for additive fabrication, modeling and control techniques for robots, and will present recent applications in medicine, industry and art.

**INF644 - Virtual/Augmented Reality & 3D Interactions  (24h, 2 ECTS), Anatole Lecuyer (Inria), Fernando Argelaguet Sanz (Inria), Maud Marchal (INSA Rennes), Guillaume Moreau (Centrale Nantes), Jean-Marie Normand (Centrale Nantes), Fabien Lotte (Inria)** (contact: Anatole.Lecuyer@inria.fr)
  Reconstructing our world or generating virtual ones would be useless without novel ways to navigate and interact with them. This course will present virtual reality systems and the associated methods for navigation and interaction, from multi-modal interaction merging visual immersion, sound and haptics systems to brain-computer interfaces.
  
==== 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
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]]** 
   
**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).

**Courses in humanities and sports (8 ECTS total)** These courses will be similar to those of the other graduate degrees at Ecole Polytechnique.
 


==== Final project (April to September - 30 ECTS) ==== 
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 (few days, in September; Alain Durmus <alain.durmus@cmla.ens-cachan.fr>)
  * INF538: Refresher in Computer science (few days, in September; christophe lino <christophe.lino@telecom-paris.fr>)



=== Period 1 ===

+ Mandatory scientific courses:

  * INF554 - Machine Learning I (Michalis Vazirgiannis, EP)
  * INF555 - Constraint-based Modeling and Algorithms for Decision Making Problems (François Fages, Sylvain Soliman, Inria)

+ Two courses among the following: 

  * INF573 - Image Analysis and Computer Vision (Renaud Keriven, EP)
  * INF574 - Digital representation and analysis of shapes (Maks Ovsjanikov, Luca Castelli, EP)
  * MAP555 - Signal processing (Olivier Rioul, Telecom ParisTech)

+ Mandatory non-scientific courses

  * MIE556 - Marketing and Strategy Introduction (P. Ginier-Gillet, Flexion)
  *  Sport
  *  Humanities
  *  Foreign languages 

 
=== Period 2 ===

+ Mandatory scientific courses: 

  * MAP569 - Machine Learning 2 (Erwan Le Pennec, EP)
  * INF585 - Computer animation (Damien Rohmer, EP)

+ Two courses among the following: 

  * INF562 - Algorithmic geometry: from theory to applications (Luca Castelli, EP) 
  * INF584 - Image synthesis: Theory and practice (Tamy Boubekeur, Telecom ParisTech)
  * MAP566 - Statistics in action (Marc Lavielle, Inria)
  * INF581 - Advanced Topics in Artificial Intelligence (Michalis Vazirgiannis, EP)

+ Mandatory non-scientific courses

  *  MIE564 - Technology-based entrepreneurship and new business creation (Erno Tornikoski, University of Exeter Business School)
  *  Sport 
  *  Humanities 
  *  Foreign languages 

=== Period 3 ===

MAP/INF590 - Internship (4 to 6 months)
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