Computer Science | Subject

Data Management & Web Design Database Management and Programming Distributed Algorithms Basic Algorithms & Programming Algorithlic / C ++ Programming Information System Advanced learning models

Statistical learning is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome. This course gives an introduction to this exciting field, with a strong focus on kernels methods and neural network models as a versatile tools to represent data This course deals with: Topic 1: Neural networks : Basic multi-layer networks / Convolutional networks for image data / Recurrent networks for sequence data / Generative neural network models Topic 2: Kernel methods : Theory of RKHS and kernels / Supervised learning with kernsl / Unsupervised learning with kernels / Kernels for structured data / Kernels for generative models It is composed of 18 hours lectures. Evaluation : There will be a written homework with theoretical exercises. In addition the students participate in a data challenge in which they implement a machine learning method of choice to solve a prediction problem on a given dataset. Both elements contribute equally to the final grade. See course website. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IGNGW6A0 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Bérengère DUC
ri-im2ag@univ-grenoble-alpes.fr

Advanced models and methods in operations research

This course presents advanced methods and technics for Operations Research. Reminder : Linear Programming, Dynamic Programming, MIP modelling and BB Complexity (P, NP, Co-NP) Advanced MIP : formulation, cuts, bounds, applications, lagragian relaxation, column generation Benders decomposition, Solvers Constraint Programming Heuristics local search approximation algorithms Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IGW1U3SV 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Advanced security

The advanced security module proposes to investigate deeper certain topics in security which include privacy models (k-anonymity, differential privacy and privacy by design), secure data structures (hash chain, Merkle’s tree), (in)secure communication protocols (WEP and WPA protocols) and anti-viruses. The module focuses on several case study on privacy enhancing technologies (PETs), blockchain (along with an overview of cryptocurrencies), wireless attacks with scapy, malware detection using YARA and ClamAV. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IFNPFKFJ 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Autonomous robotics

A robot is a mechatronic system with perception, decision and action capabilities design to perform in an autonomous way different tasks in the real world. Whatever the robot (e.g. mobile robot, industrial arm, mobile manipulator) and the task that it has been assigned, the robot will have to move (move its whole body or a part of its body, e.g. arm, hand). Accordingly, motion autonomy is an essential skill for a robot. To achieve motion autonomy, it is required to solve a number of challenging problems in areas as diverse as sensor data processing, world modeling, motion planning, obstacle avoidance and control. The purpose of the course is to present the main concepts, tools and techniques that Roboticists have developed in the past fifty years in order to address these challenges. The course has three parts that focus on different aspects: The first part is about robot state estimation and world modeling. It presents the most popular approaches to perform state estimation. The basic equations of the Bayes filter are derived first. Then, the Extended Kalman Filter is introduced. These methods are then used to explore the following fundamental estimation problems: 1) robot localization, 2) Simultaneous Localization and Mapping (SLAM), 3) cooperative localization, and 4) simultaneous localization and self-calibration. The structural properties of these problems are studied. In particular, it is shown how the computational complexity scales with the size of the state. Finally, more theoretical aspects related to estimation with special focus on state observability are discussed. The second part focuses on the decision-making aspects. Motion planning is addressed first in the seminal configuration space framework, the main configuration space-based motion planning techniques are reviewed. Then, to deal with the uncertainty of the real world and the discrepancy between the world and its model, reactive collision avoidance techniques are presented. Finally, motion safety is formally studied thanks to the Inevitable Collision State concept. The third part is an introduction to control theory for articulated robots. The objectives are to understand basic concepts about the kinematics and dynamics of articulated robots and basic control theory in order to approach classical control methods, as well as a few selected advanced topics. The kinematics of articulated robots is introduced first, covering advanced topics such as singularities, hierarchies of objectives, inequality constraints. A brief reminder about Newton, Euler and Lagrangian equations of motion as well as basic Lyapunov stability theory is also provided before discussing standard motion control schemes such as Proportional-Derivative, Computed Torque, Operational Space and Task Function approaches. Advanced topics such as space robots, biped robots, Viability theory and optimal control are also touched. Évaluation: examen final écrit (3h) + examen de rattrapage écrit (1,5h) ou oral. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IGDGFN4Q 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Computer vision

This course provides an introduction to computer vision. It concerns techniques for constructing systems that observe and recognize objects, scenes and activities. It provides training in tools and techniques and models for: the image formation process, color and illumination, image signal processing, multi-scale image description, image analysis, object detection, recognition and tracking, motion capture, modeling and understanding, image matching, multi-camera systems, and 3D reconstruction and modeling. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IGDGATTZ 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Data management in large-scale distributed systems

Target skills : Data management and knowledge extraction have become the core activities of most organizations. The increasing speed at which systems and users generate data has led to many interesting challenges, both in the industry and in the research community. The data management infrastructure is growing fast, leading to the creation of large data centers and federations of data centers. These can no longer be handled exclusively with classic DBMS. It requires a variety of flexible data models (relational, NoSQL…), consistency semantics and algorithms issued by the database and distributed system communities. In addition, large-scale systems are more prone to failures, and should implement appropriate fault tolerance mechanisms. The dissemination of an increasing amount of sensors and devices in our environment highly contribute to the “Big Data” and the development of ubiquitous information systems. Data is processed in continuous streams providing information related of users context, such as their movement patterns and their surroundings. This data can be used to improve the context awareness of mobile applications and directly target the needs of the users without requiring an explicit query. Combining large amounts of data from different sources offers many opportunities in the domains of data mining and knowledge discovery. Heterogeneous data, once reconciled, can be used to produce new information to adapt to the behavior of users and their context, thus generating a richer and more diverse experience. As more data becomes available, innovative data analysis algorithms are conceived to provide new services, focusing on two key aspects: accuracy and scalability. Program summary : In this course, we will study the fundamentals and research trends of distributed data management, including distributed query evaluation, consistency models and data integration. We will give an overview of large-scale data management systems, peer-to-peer approches, MapReduce frameworks and NoSQL systems. Ubiquitous data management and crowdsourcing will also be discussed. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IH353SDE 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.