Course objectives: The goal of this course is to develop the necessary skills of the business entrepreneur, to generate and evaluate innovative ideas, to develop and materialize innovation in products and services, and to structure a business plan to incubate and explore technology based innovation. This teaching is voluntarily oriented towards the acquisition of specific knowledge of market mechanisms, financing, and management. Main concepts, theories and mechanisms of entrepreneurship and innovation will be studied to offer a broad view of the strategic role of innovation. Technological trajectories, the concept of open innovation, the innovation process, the diffusion of innovation and new business models and markets segments will be approached.
Course description: This course is focused on entrepreneurship and innovation, what it is (or not), how it appears (“search” & “select”), and how it can be managed (“implement” and “capture”). The course is committed to providing an opportunity to learn to use some tools and news ways of thinking which are better suited to addressing complex problems and opportunities inherent in organizations today. In terms of method, the course will promote interactions between students and the professor. During classroom sessions, students will participate and sometimes work in groups to answer specific questions, and discuss what they have learned. Concepts and ideas will be illustrated with concrete examples and some press releases. Using learning by doing methods, students will be taught business planning, particularly focusing on innovation projects. Students will develop a value and propose an elevator pitch. They will also put into practice the knowledge acquired through case studies.
Key skills acquired: After completing this course, the students will be able to:
1. Understand the ins and outs of an entrepreneurial approach
2. Differentiate the different types of business plan
3. Develop a business plan
4. Create an innovative value proposition
5. Present a project using professional presentation skills
The course will present the main paradigms of automatically learning from data. The student will gain an understanding about the implications of working with high-dimensional and/or big amounts of data. In the course we will implement and apply basic algorithms to perform classification, regression and density estimation. Students will be capable of analyzing and explaining results of applying machine learning techniques. They will be able to identify over- and under- fitting and reason in terms of bias and variance of errors. The methods will be illustrated using publicly available software tools and data sets used to perform analysis on large volumes of data.
Introduction du Physical Oceanography, the time-space scales, the main processes, the instrumentation.Practical work at sea, data analysis.The Great Garbage, what is the situation, how physical oceanographers study the marine plastic pollution
The objective of this lecture is to provide insight specific to deep-sea underwater vehicles and their operation. It gives an overview of existing underwater vehicles, observatories and operations procedures, with emphasis on the Underwater Systems developed and operated within the French Oceanographic Fleet. Sensors and equipment required for deep water submarine operations will be presented. A focus on acoustic communication necessary for the operation of AUVs (Autonomous Underwater Vehicles) will be carried out in terms of principle, strategies and equipment. Instrumental architecture and key principles of distributed multi-sensor acquisition systems will be exposed. Visual perception in deep-sea operations as well as 3D representation of natural structures on the ocean floor will also be deepened
This module aims at equipping students with the ability of creating machine learning (and deep learning) models for usual intelligent robotic perception tasks. Computer vision and machine listening on a marine environment are the two target fields of application of the contents of this course, however other modalities (e.g. hyperspectral imaging, natural language,...) will be discussed. After the module students will be capable at identifying perception tasks to which deep learning techniques can be applied. Students will be able to build specific models to solve these problems and assess their performance. They will also know the main specificities of image and audio for marine applications.
Participants will carry out tutorial and practical work on marine and underwater robotsunder the supervision of lecturers. Matlab/Simulink and Ubuntu/ROS will be used to take over the following robotic platforms :• Marius sailing robot• 8-thruster BlueROV underwater robot• 3-thruster fully equipped CORAL underwater robot
This course presents the main framework of reinforcement learning (RL). At the end of the course, the student will be able to identify and frame problems under this formalism. He will master the vocabulary associated with the RL field, and will be aware of the main obstacles to tackle when using and developing RL approaches (e.g. exploitation-exploration trade-off, sample efficiency). Moreover students will be capable of understanding and implementing “vanilla” algorithms of the core methods in RL (e.g. Q-learning and policy optimisation) and analyze/apply some specific advanced algorithms (e.g. A2C and PPO).
Module servant à recueillir tous les commentaires des étudiants du MIR, depuis les évaluations des matières jusqu'aux choix des options d'études.
After this course the students will be able to identify the deep learning (DL) approaches to be applied to multiple types of machine learning problems, depending on the task and the data inputs/outputs. Students will know how to build and train advanced DL models with the use of existing publicly available software tools. We will also introduce the main shortcomings and limitations of deep learning techniques such as the problem of interpretation and the exploitation of adversarial attacks.
Objectives:The aim of this course is to give the students understanding of both Physics of acoustic waves and signal processing techniques, in order to be able to suggest which system is appropriate for given specifications, to predict its performances through simple models, to implement data processing algorithms and to interpret the results. The complementarity of deep learning techniques with physics based approach is highlighted.Contents:- Propagation of acoustic waves in shallow and deep water, scattering, Doppler effect.- Signal processing : positioning, detection and ranging, noise correlation, time reversal.