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Machine Learning Fundamentals

Machine Learning is one of the key areas of Artificial Intelligence and it concerns the study and the development of quantitative models that enables a computer to perform tasks without being explicitly programmed to do them. Learning in this context is hence to recognize complex forms and to make intelligent decisions. Given all existing entries, the difficulty of this task lies in the fact that all possible decisions is usually very complex to enumerate. To get around that, machine learning algorithms are designed in order to gain knowledge on the problem to be addressed based on a limited set of observed data extracted from this problem.

http://ensimag.grenoble-inp.fr/fr/formation/machine-learning-fundamentals-wmm9mo21 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The intent of this course is to propose a broad introduction to the field of Machine Learning, including discussions of each of the major frameworks, supervised, unsupervised and semi-supervised learning. Massih-Reza Amini 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). Final exam international.cic_tsukuba@grenoble-inp.fr

Fundamentals of Probabilistic Data Mining

This lecture introduces fundamental concepts and associated numerical methods in model-based clustering, classification and models with latent structure. These approaches are particularly relevant to model random vectors, sequences or graphs, to account for data heterogeneity, and to present general principles in statistical modelling.

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Model-based clustering, classification and models with latent structure are particularly relevant to model random vectors, sequences or graphs, to account for data heterogeneity, and to present general principles in statistical modelling. The following topics are addressed:

Principles of probabilistic data mining and generative models; models with latent variables
Probabilistic graphical models
Mixture models and clustering
PCA and probabilistic PCA
Generative models for series and graphs : hidden Markov models Fundamental principles in probability theory (conditioning) and statistics (maximum likelihood estimator and its usual asymptotic properties).
Constrained optimization, Lagrange multipliers. Jean-Baptiste Durand 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.

Information access and retrieval

This course addresses advanced aspects of information access and retrieval, focusing on several points: models (probabilistic, vector-space and logical), multimedia indexing, web information retrieval, and their links with machine learning. These last parts provide opportunities to present the processing of large amount of partially structured data. Each part is illustrated on examples associated with different applications.

http://ensimag.grenoble-inp.fr/fr/formation/information-access-and-retrieval-wmm533u Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The domain of information access encompasses several applications pertaining to categorization, clustering or information retrieval. The goal of this module is to present models and algorithms used in these frameworks, and is intended to students willing to use or develop tools for data mining, machine learning and information retrieval. This course requires knowledge of probability and integration theory. Some previous knowledge of Stochastic processes is welcomed. No previous knowledge of Brownian motion or Stochastic Calculus is required. Georges Quenot 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.

Knowledge representation and reasoning

The course covers knowledge representation and reasoning algorithms in artificial intelligence. The focus is, in the first part, on logical and symbolic knowledge and, in a second one probabilistic knowledge. The course will cover logical languages, symbolic languages, probabilistic systems, and decision making with these languages and systems.

http://ensimag.grenoble-inp.fr/fr/formation/knowledge-representation-and-reasoning-wmm533b-1 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Danielle Ziebelin 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.

Semantic Web : from XML to OWL

The web has been constantly evolving from a distributed hypertext system to a very large information processing machine. As fast as it is, this evolution is grounded on theoretical principles borrowing to several fields of computer science such as programming languages, data bases, structured documentation, logic and artificial intelligence. The smooth operation of the past and future web at a large scale is relying on these foundations. The goal of this course is to present them, the problem that they solve as those that they uncover. It considers three milestones of this evolution: XML, the social web and the semantic web.

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The first part aims at introducing programming language foundations, algorithms and tools for processing tree-structured information, and for the analysis of queries and programs that manipulate trees. This part consists in an introduction to relevant theoretical tools with an application to NoSQL and XML technologies in particular. The theoretical part introduces tree grammars, finite tree automata, classical tree logics and a recent mu-calculus of finite trees, in connection to practical problems and technologies such as XPath/XQuery, DTD, schemas, etc. Applications are illustrated through scalable validation of document streams, efficient query evaluation, static analysis of expressive queries in the presence of constraints, and static type-checking of programs manipulating labeled trees. The course also aims at presenting challenges, important results, and open theoretical issues in the area of NoSQL programming.

The second part summarizes data models and algorithms required to extract, manage and access massive amounts of social content. The course examples are drawn from real-world applications such as URL search and recommendation on Delicious, group recommendation in MovieLens and extracting travel itineraries from Flickr photos. The course goals are: acquire knowledge on scalable algorithms for processing large volumes of social data and extracting value from that data and learn how to run and interpret large-scale user studies.

The third part introduces the semantics of knowledge representation on the web. The semantic web extends the web with richer and more precise information because it is expressed in a formal language using a vocabulary defined in an ontology (a structured vocabulary of concepts and properties defined in a logic). Ontologies are used for describing web resource content and reasoning about these resources formally. We introduce the semantic web languages (RDF, RDFS, OWL) and show their relations with knowledge representation formalisms (conceptual graphs, description logics) and XML. This provides tools for reasoning with ontologies and, in particular, to evaluate queries. However, the distributed nature of the web leads to heterogeneous ontologies which must be matched before using them. We discuss ontology matching and explain how to semantically interpret the relations between ontologies. Finally, this is applied to network of peers using knowledge together. Sihem Amer Yahia 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 Algorithms for Machine Learning and Data Mining

A prior algorithms (Frequent item sets) & Page Rank, Monte-carlo, MCMC methods: Metropolis-Hastings and Gibbs Sampling, Matrix Factorization (Stochastic Gradient Descent, SVD), Generalized kmeans and its variants (Bach, Online, large scale), Kernel clustering (Support Vector Clustering), Spectral clustering, Classification and Regression Trees, Support Vector regression,Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Fundamentals of probability/statistics, linear algebra and computer science (data structures and algorithms) Eric Gaussier 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.

iDesigner : Tackling Complexity by Integration

Designing modern products and systems in an innovative, sustainable and
competitive way demands the implementation of new paradigms in
development organisations. Design is no longer concentrated on a
specific phase. It goes beyond aesthetics to cover all functional
aspects of a product or a system, thus driving the entire development
process. Consequently, more and more actors of the complete product
life-cycle have to be integrated in the design process. This creates new
challenges for design engineers, as well as for development project
managers. This course adresses all these challenges looking at examples
from the design of automotive mechatronic systems.

http://genie-industriel.grenoble-inp.fr/en/studies/idesigner-tackling-complexity-by-integration-5guc0904 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres The definition and motivation of integration in design:

• the targets of integration, including the product life-cycle,
• essential methods of integration, including concurrent engineering and product modelling,
• mastering complexity and innovation,
• knowledge management for integration,
• collaborative integrated design
•
selected aspects of integration in design, including sustainable
design, risk assessment, safety design, virtual development tools and
techniques, etc… Basic knowledge on the product design process. Andreas RIEL 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 Control : Methods and Practical Implementation Tools

1) Predictive control : Illustrative example ; Prediction equations for linear time invariant systems ; Definition of the cost function ; Link with the unconstrained optimal regulator ; Constraints definition ; Constrained predictive control ; Control parametrization ; Application examples ; Nonlinear Predictive control
2) Model-based Diagnosis : Introduction, basic concepts, motivation and preliminaries: fault detection and isolation and its use for fault-tolerance and complex systems monitoring and safety. Process models and fault modelling. Presentation of the different approaches and focus on the model-based approach. ; Data validation and reconciliation: measurement errors, balance equations, state estimation for constrained and unconstrained systems, linear and bilinear systems ; Fault detection with parity equations – Static and dynamic cases: Analytical redundancy, parity equations and generation of residuals. Enhanced and structured residuals. Properties and analysis of residual signals ; Fault detection and isolation with state observers and state estimation. Unknown inputs observers. Observers banks.

3) Embedded system code design & implementation : Real-time and Embedded Systems design : Real-Time scheduling algorithms on uni and multiprocessor systems, programming techniques

http://ense3.grenoble-inp.fr/en/academics/advanced-control-methods-and-practical-implementation-tools-5eus5aua The aim of this course is to present advanced control systems methods for optimal and predictive control and fault detection and isolation & fault tolerance. Tools and methods for real-time implementation of control algorithms on embedded systems are also presented Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique The aim of this course is to present advanced control systems methods for optimal and predictive control and fault detection and isolation & fault tolerance. Tools and methods for real-time implementation of control algorithms on embedded systems are also presented Basic course in control systems, scientific programming and real-time computer systems Christophe Berenguer 5 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). Session 1 : 60%CT + 40% CC
Session 2 : R Remplace CT international.cic_tsukuba@grenoble-inp.fr

Foresight and Strategy

Issue: Understanding Opportunities and Challenges of the Industry of the Future … for Competitive Firms Design
In
2050, Manufacturing will look very different from today. Key words are
faster, responsive, clean and green, close to customers, interconnected,
smart. Firms will adapt a massive flow of new technologies: both
digital and physical.
As in the past, this 4th Industrial Revolution
will shift the role and place of the industry in society, it will
generate new business models, new forms of firm organizations and value
chains, new sources of value. For successful firms, strategic choices
will be crucial.
This course is designed to help students
forecasting the future, and acquiring the skills to develop strategic
choices in the coming environment. A good strategy traces the paths to
innovation: products, processes, organizations, value chains.

http://genie-industriel.grenoble-inp.fr/fr/formation/ue-foresight-and-strategy-wgu2str7 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres List of 11 themes (Part II)
I. Final Products 1. Individualization
II. Final Product 2. Interconnection, Ecosystem
III. Final Product 3. Matching (Including C2C)
IV. Value Chain 1. Vertical Relations
V. Value Chain 2. Externalities, Ecosystem
VI. Value Chain 3. Capturing Value and Leadership
VII. Value Chain 4. Standards, Norms, and Interoperability
VIII. New Production Process 1. The Future of Work
IX. New Production Process 2. Explore and Exploit, Complexity in Dynamics
X. New Production Process 3. Disruption and Continuity, Innovation and Growth
XI. Macro Issues and Politics Microeconomics, organisation and basic management are welcomed but not compulsory Bernard RUFFIEUX 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.

Mechatronics

Modelisation, simulation and conception of mechatronic systems. Industrial and mobile robotics notions. Specification and evaluation of dynamical performances. Embedded control systems and reference generation. Disturbance estimation and adaptive rejection of disturbances. Fault-tolerant control notions. Code implementation in microprocessors.

http://ense3.grenoble-inp.fr/en/academics/mecatronics-5eus5met study a mechatronics approach for conceiving and for dimensioning intelligent systems. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique The main objective is to study a mechatronics approach for conceiving and for dimensioning intelligent systems.
UE Automatique 1 (Automatic control 1)
UE Actionneurs (Actuators)
UE Systèmes Temps-réel (Real-time systems) John-Jairo Martinez-Molina 5 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). DS 2h + CC en BE.
(Individual exam 2h and computer assisted exercises). international.cic_tsukuba@grenoble-inp.fr

Modelling and Optimization in Product Development

This course presents advanced techniques for modeling, simulation and
optimization in technical product development. This 3 points are viewed
as cornerstones for the DMU development and usage.Lessons are describing the concepts while lab works are centered on operating basic tools.

http://genie-industriel.grenoble-inp.fr/fr/formation/ue-modelling-and-optimization-in-product-development-wgumode9 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres System modeling and simulationProduct architectureSimulation preparation Data and process models (principles, UML), system simulationIdealization, simplification for simulationCAD simulation link
Digital mock upGeometrical aspects, creation, maintenance, Parameterization, Direct modeling Product familyHeterogeneous Data Integration (Space Claim, PTC, catia)CAD modeling API : external control
Robust and optimal designDesign structure matrix: optimization problemContinuous optimization with or without constraints: modeling, solving, constraintsConstraint satisfaction problem meta heuristic methods: annealing, genetic algorithms, PSO CAD modeling : basics about 3D modeling
Applied mathematics : derivative, integration, etc.
Computer science: scripting language: python, scilab, visual basic Frédéric NOEL 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.

River Dynamics

First part: fluvial dynamics.
1.- Introduction on the floods and floodings. Relinders on open channel hydraulics Hydraulics.
2.- One-dimensional free surface flows: the Barré de Saint-Venant PDE equations. Physical meaning of the different terms of the equations. Mathematical properties: the characteristics and the invariants.
3.- Physics of floods and their modeling. Kinematic and diffusive approximation. Example: the deterministic runoff.
4.- Flood alleviating structures: physical principles and optimisation of dams by a costs-benefits analysis
5.- Rapidly varying unsteady open channel flows dominates by inertia: shock and rarefaction waves. Sudden stop of a flow and dam breakage.

Second part: sediment transport.
1. Fundamental concepts: the various modes of sediment transport, the materials, introduction to river morphology.
2. Elementary analysis of the bed-load mechanism: threshold conditions of sediment movement, sensitivity of Meyer-Peter and Muller formula. The Einstein formula
3. 1D analysis of sediment transport: predictive formulaes and their range of applicability.
4. Deeper in the mechanisms of the sediment transport: transport in suspension, grain roughness, shape roughness, dune and bed forms, secondary currents, transported grain size distribution and bottom grain size distribution, grain size sorting by the flow, hiding effects & armoring.
5. Morphological changes

http://ense3.grenoble-inp.fr/en/academics/river-dynamics-5eus5dyf-1 First part: fluvial dynamics.
Understand the physics and the modeling of unsteady flows in the rivers and canals (propagation of the tide, floods and of rapidly varying flows in the rivers and canals). Saint Venant equation formulation.
Design the volume of retention dams for flood protection.
Understanding the links between the physical reality, its perception and its modeling.
Brief presentation of the market software properties dealing with this problem.

Second part: sediment transport.
Students will become acquainted with the pluridisciplinary aspects of this topic.
Student will be asked to master: the concept and the quantitave determination of sediment mouvement inception, computation of sediment transport rates, the concept of sedimentary equilibrium (river bed slope, grain size distributions), engineering tools of the field Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique First part: fluvial dynamics.
Understand the physics and the modeling of unsteady flows in the rivers and canals (propagation of the tide, floods and of rapidly varying flows in the rivers and canals). Saint Venant equation formulation.
Design the volume of retention dams for flood protection.
Understanding the links between the physical reality, its perception and its modeling.
Brief presentation of the market software properties dealing with this problem.

Second part: sediment transport.
Students will become acquainted with the pluridisciplinary aspects of this topic.
Student will be asked to master: the concept and the quantitave determination of sediment mouvement inception, computation of sediment transport rates, the concept of sedimentary equilibrium (river bed slope, grain size distributions), engineering tools of the field – Open channel hydraulics
– Fluid mechanics and turbulence
– Hyperbolic partial differential equations (characteristics)
– Statistics Eric Barthelemy 5 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). CT: 2x2h sitting exam;
CC: 2 practicals (TP) & a mini project (BE);
final mark: 75% CT + 25% CC international.cic_tsukuba@grenoble-inp.fr