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}/ * Do not edit these comments. * * Syntax of dokuwiki: see https://www.dokuwiki.org/wiki:syntax * * To refer to a section named My Section in “page”, use page#my_section * */ ====== Latest news ====== ====== Tracks in semester 9: 2021-2022 ====== The first semester of MSIAM master 2 is essentially divided in two tracks. Each student should be registered in one of the following tracks: * Modelling, Scientific Computing and Image analysis (MSCI). The provisional course schedule is available {{ :m2common:edt_m2-msci_s9-2021-22.pdf |here} * [[m2courses#Data_Science_(DS)|Data Science]]. The provisional course schedule is available {{ :m2common:edt_m2-ds_s9-2021-22.pdf |here} However a personalized track may also be build for some students from the available courses (if no timetable conflicts appears). The personalized tracks must be approved by the Professors in charge of MSIAM. Academic courses: * 30 ECTS scientific courses (3 or 6 each, excluding French language course). * 6 ECTS may be chosen by the students outside of the MSIAM offer (needs no timetable conflict and approval by the MSIAM heads). Visit for example the current [[https://www-fourier.ujf-grenoble.fr/m2r/|fundamental mathematics offer]]. ====== Semester 10: 2020-2021 ====== [[aboutmasterproject|MSc thesis: rules, advice for guidance, documents and schedule of defences]] ====== Refresher courses ====== Refresher courses can be followed at the beginning of semester 9, to be chosen among: * [[lectures#Introduction_to_matrix_numerical_analysis|Introduction to matrix numerical analysis]] * [[lectures#Introduction_to_numerical_optimisation|Introduction to numerical optimisation]] These refresher courses do not count in the total of 30 required ECTS. ---- /** * ====== Industrial conferences 2017-8 ====== * (//you are free to participate//) * * At UFR IMAG (in French) * * Human talk * * Les métiers du numérique : le 12 octobre, 11h30, UFR IM2AG bâtiment F amphi 018(pour 1h), avec la participation de Anatoscope, Capgemini, Hardis Group, SurgiQual * * [[https://im2ag.univ-grenoble-alpes.fr/menu-principal/presentation/actualites/human-talk-big-data-le-jeudi-9-novembre-2017-269371.kjsp?RH=1485265166783|Le big data : le 9 novembre, 11h30 (pour 1h), amphi F018]] * * la sécurité des SI : le 16 novembre, 11h30 (pour 1h) * * Le cloud : le 15 février, 11h30 (pour 1h) * * Industrie 4.0 : le 1er mars, 11h30 (pour 1h) * * Ateliers RH : les 19 octobre et 23 novembre (13h-18h) * * At ENSIMAG (probably in French too) * * 13/10 CIC_IT et IMAC-Imag - "Imagerie médicale" - Amphi D Ensimag * * 20/10 NEOVISION - "IA, machine learning, computer vision" - Amphi E Ensimag * * 10/11 QUANTMETRY - "Data Science et Data Engineering" * * 24/11 COMSOL - "Calcul scientifique" * * 01/12 INOPRO - Calcul scientifique * * 15/12 PROBAYES - A confirmer */ ===== Modelling, Scientific Computing and Image analysis (MSCI) ===== Modelling, Scientific Computing, Imaging, Geometry, CAD have been for decades at center of many innovations in many areas: design and development in industry such as transports, manufacturing (any innovative object is concerned by MSCI and CAD), Medical / Pharmaceutical (modelling of systems [CT scanner, MRI, hybrid imaging, robots, etc.], life and biomedical modelling), Chemical (modelling and simulation of reactions), Environment, Big Data (data and image modelling and analysis) … The purpose of the MSCI track is to train both high-level researchers and engineers in Modelling, Scientific Computing, Imaging, by providing theoretical foundations and applied methodology. The theoretical courses (** 144h to 180h **) may be completed by more in-depth study of some courses and associated projects or projects from the Industry (see [[lectures#Modelling_Seminar_and_Projects|Modelling Seminar and Projects]]). They are followed by an internship in a research lab or company. This track is preparing students both for research in applied maths and also for high level applications of mathemetics, modelling and computing in wide areas in the industry. * [[lectures#Advanced_Imaging|Advanced Imaging]] * [[lectures#An introduction to shape and topology optimization|An introduction to shape and topology optimization]] NEW in 2020 * [[lectures#Congestion Phenomena and Compressibility for Granular Media|Congestion Phenomena and Compressibility for Granular Media]] NEW in 2020 * [[lectures#Efficient_methods_in_optimization|Efficient methods in optimization]] * [[lectures#Geophysical imaging|Geophysical imaging]] NEW in 2020 * [[lectures#GPU_computing|GPU computing]] * [[lectures#Level_set_methods_and_optimization_algorithms_with_applications_in_imaging|Level set methods and optimization algorithms with applications in imaging]] * [[lectures#Model_exploration_for_approximation_of_complex_high-dimensional_problems|Model exploration for approximation of complex, high-dimensional problems]] * [[lectures#Modelling_Seminar_and_Projects|Modelling Seminar and Projects]] * [[lectures#Non-smooth Convex Optimization Methods|Non-smooth Convex Optimization Methods]] * [[lectures#Numerical_optimal_transport_and_geometry|Numerical optimal transport and geometry]] * [[lectures#Software_Development_Tools_and_Methods|Software Development Tools and Methods]] * [[lectures#Wavelets_and_applications|Wavelets and applications]] /* * Computational Geometry (see [[http://www-ufrima.imag.fr/ue/WebFormation/ue.php?code=GINF538S&ismat=&lang=en| MOSIG: Computational Geometry]]) (WARNING: this course will not be available in this list in 2018) * [[lectures#High-performance_exact_computations|High-performance exact computations]] * [[lectures#Stochastic approaches for uncertainty quantification|Stochastic approaches for uncertainty quantification]] * [[lectures#High_resolution_seismic_imaging_by_waveform_inversion|High resolution seismic imaging by waveform inversion]] */ /* en 2016-7 * [[lectures#Mathematical_modelling_in_life_science:_reaction-dispersion_models| Mathematical modelling in life science: reaction-dispersion models]] * [[lectures#Scientific_Computing|Scientific Computing]] * [[lectures#Computer_Aided_Design_and_geometric_modelling|Computer Aided Design and geometric modelling]] * [[lectures#Curve_and_surface_reconstruction|Curve and surface reconstruction]] */ /* * [[lectures#Scientific_visualization|Scientific visualization]] * [[lectures#Numerical_methods_for_hyperbolic_equations|Numerical methods for hyperbolic equations]] * [[lectures#Kinetic_equations_and_conservation_laws|Kinetic equations and conservation laws]] * [[lectures#Numerical_methods_for_electromagnetism|Numerical methods for electromagnetism]] * [[lectures#Dynamical_systems,_bifurcations_and_applications|Dynamical systems, bifurcations and applications]] * [[lectures#Mathematical_methods_for_wave_propagationapplication_to_inverse_problems_and_medical_imaging|Mathematical methods for wave propagation: application to inverse problems and medical imaging]] * [[lectures#Complex_fluid_modelling|Complex fluid modelling]] * [[lectures#Model_Coupling|Model Coupling]] * [[lectures#Curve_and_surface_reconstruction|Curve and surface reconstruction]] */ /* ===== Geometry, Image and CAD (GICAD) ===== * [[lectures#Software_Development_Tools_and_Methods|Software Development Tools and Methods]] * [[lectures#Efficient_methods_in_optimization|Efficient methods in optimization]] * [[lectures#High_Performance_Computing_for_Mathematical_Models|High Performance Computing for Mathematical Models]] * [[lectures#High-performance_exact_computations|High-performance exact computations]] * [[lectures#Inverse_methods_and_data_assimilation|Inverse methods and data assimilation]] * [[lectures#High_resolution_seismic_imaging_by_waveform_inversion|High resolution seismic imaging by waveform inversion]] * [[lectures#Medical_Imagingtomography_and_3D_reconstruction_from_2D_projections|Medical Imaging: tomography and 3D reconstruction from 2D projections]] * [[lectures#Optimal_Transport,_level­setapplications_to_image|Optimal Transport, level­set: applications to image]] * [[lectures#Wavelets_and_applications|Wavelets and applications]] * [[lectures#Advanced_Imaging|Advanced Imaging]] * [[lectures#Curve_and_surface_reconstruction|Curve and surface reconstruction]] * [[lectures#Scientific_visualization|Scientific visualization]] */ /* * [[lectures#Surface_modelling|Surface modelling]] */ ---- ===== Data Science (DS) ===== The burst of data collection at unprecedented speed and scale in many fields, from biology to astrophysics, demands a paradigm shift in applied mathematics and computer science in order to face the new challenges in scientific modelling and computation. To harness the power of this data revolution, the world needs academic researchers and professionals called "data scientists" skilled in designing and utilizing automated methods of analyzing it. The Data Science track in the MSIAM master aims at establishing the country’s leading Data Science academic training. Data science is becoming essential to answer some of the big scientific questions and technological challenges of our times: How can we prevent cancer and find better cures for diseases? How does the brain work? How can we design an artificial intelligence? Data science lies at the crossroad of mathematics (pure and applied), statistics, computer science and an increasingly large number of application domains. The University of Grenoble Alpes benefits from a very active community in data science, whose most visible banner is the [[https://data-institute.univ-grenoble-alpes.fr/|Grenoble Data Science Institute]]. Among its permanent groups and recurrent activities are the [[https://data-institute.univ-grenoble-alpes.fr/education/data-club/|Grenoble Data Club]] and [[https://data-institute.univ-grenoble-alpes.fr/education/r-in-grenoble/|R-in-Grenoble]] seminars. The Data Science track has common courses with the [[http://mosig.imag.fr/|MoSIG program]]. The Data Science track is both research- and industry-oriented. Its purpose is to train high-level researchers with skills in both the mathematical aspects of Data Science, Probability and Statistics, and in practical skills in data analysis and programming. The theoretical courses **(~180h)** are followed by an internship in a research lab or company. Some courses in DS focus on the methods and mathematical results on which rely the main approaches in machine learning, optimization and Probability and Statistics. They are oriented towards acquiring knowledge in machine learning, probabilistic and statistical modelling and optimization. Some others focus on large-scale (often meaning high-dimensional) aspects of data science. They are dedicated to large-scale databases, optimization and machine learning. Some of them focus on some given applications, such as biology, information retrieval in multimedia databases or object recognition in images (typically, using deep learning approaches). ** Important note in 2021-2022 :** the two couples of courses (1) Non-smooth Convex Optimization Methods + (2) Efficient methods in optimization (1) Model exploration for approximation of complex, high-dimensional problems + (2) Inverse problem and data assimilation : variational and Bayesian approaches are thought so that (2) follows (1) and it makes sense to take (1)+(2) to have a deep view on the subject. Students are not forced to take (1)+(2), they can take for example just (1). But note that it will be difficult to follow (2), without having taken (1). ** More information about the interplay of the courses here: ** TO BE ANNOUNCED Proposed courses: * [[lectures#Advanced_algorithms_for_machine_learning_and_data_mining|Advanced algorithms for machine learning and data mining]], common MOSIG * [[lectures#An introduction to shape and topology optimization|An introduction to shape and topology optimization]] NEW in 2020, common MSCI * [[lectures#Computational_biology|Computational biology]] * [[lectures#Data_Science_Seminar|Data science seminar]] * [[lectures#Efficient_methods_in_optimization|Efficient methods in optimization]], common MSCI * [[lectures#Fundamentals_of_probabilistic_data_mining|Fundamentals of probabilistic data mining]] * [[lectures#GPU_Computing|GPU Computing]], common MSCI * [[lectures#Information_access_and_retrieval|Information access and retrieval]], common MOSIG * [[lectures#Introduction to extreme-value analysis|Introduction to extreme-value analysis]] NEW in 2020 * [[lectures#Inverse problem and data assimilation : variational and Bayesian approaches |Inverse problem and data assimilation : variational and Bayesian approaches ]] NEW in 2021 * [[lectures#Kernel methods for machine learning|Kernel methods for machine learning]] * [[lectures#Machine_Learning_Fundamentals|Machine Learning Fundamentals]], common MOSIG * [[lectures#Model_exploration_for_approximation_of_complex_high-dimensional_problems|Model exploration for approximation of complex, high-dimensional problems]] * [[lectures#Model_selection_for_large-scale_learning|Model selection for large-scale learning]] * [[lectures#modelling_seminar_and_projects|Modelling Seminar and Projects]] * [[lectures#Non-smooth Convex Optimization Methods|Non-smooth Convex Optimization Methods]] NEW in 2021, common MSCI * [[lectures#Numerical_optimal_transport_and_geometry|Numerical optimal transport and geometry]], common MSCI * [[lectures#Reinforcement learning|Reinforcement learning]], common MOSIG * [[lectures#Software_Development_Tools_and_Methods|Software Development Tools and Methods]] * [[lectures#Statistical methods for forecasting|Statistical methods for forecasting]] NEW in 2020 * [[lectures#Temporal and spatial point processes|Temporal and spatial point processes]] NEW in 2021 * [[lectures#Wavelets_and_applications|Wavelets and applications]], common MSCI ---- [[:lectures|Global list of courses]] ---- ===== Advice and testimonials (Alumni) ===== Former students maintain a webpage providing testimonials, assessment of the training program and advice for incoming students : https://msiam.imag.fr/m2advice_testimonials

m2courses.1630410679.txt.gz · Last modified: 2021/08/31 13:51 by picard
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