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m2courses [2021/04/29 09:37] oudet [Modelling, Scientific Computing and Image analysis (MSCI)] 
m2courses [2021/08/31 13:55] (current) picard 
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====== Tracks in semester 9: 20202021 ======  ====== Tracks in semester 9: 20212022 ====== 
 
The first semester of MSIAM master 2 is essentially divided in two tracks.  The first semester of MSIAM master 2 is essentially divided in two tracks. 
Each student should be registered in one of the following tracks:  Each student should be registered in one of the following tracks: 
* [[m2courses#Modelling,_Scientific_Computing_and_Image_analysis_(MSCI) Modelling, Scientific Computing and Image analysis (MSCI)]]  * [[m2courses#Modelling,_Scientific_Computing_and_Image_analysis_(MSCI) Modelling, Scientific Computing and Image analysis (MSCI)]]. The provisional course schedule is available {{ ::m2common:edt_m2msci_s9202122.pdf here}} 
* [[m2courses#Data_Science_(DS)Data Science]]  * [[m2courses#Data_Science_(DS)Data Science]]. The provisional course schedule is available {{ ::m2common:edt_m2ds_s9202122.pdf here}} 
 
However a personalized track may also be build for some students from the available courses (if no timetable conflicts appears).  However a personalized track may also be build for some students from the available courses (if no timetable conflicts appears). 
* 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://wwwfourier.ujfgrenoble.fr/m2r/fundamental mathematics offer]].  * 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://wwwfourier.ujfgrenoble.fr/m2r/fundamental mathematics offer]]. 
 
====== Semester 10: 20192020 (20202021: upcoming ...)======  ====== Semester 10: 20202021 ====== 
[[aboutmasterprojectMSc thesis: rules, advice for guidance, documents and schedule of defences]]  [[aboutmasterprojectMSc thesis: rules, advice for guidance, documents and schedule of defences]] 
 
The University of Grenoble Alpes benefits from a very active community in data science, whose most visible banner is the [[https://datainstitute.univgrenoblealpes.fr/Grenoble Data Science Institute]]. Among its permanent groups and recurrent activities are the [[https://datainstitute.univgrenoblealpes.fr/education/dataclub/Grenoble Data Club]] and [[https://datainstitute.univgrenoblealpes.fr/education/ringrenoble/RinGrenoble]] seminars.  The University of Grenoble Alpes benefits from a very active community in data science, whose most visible banner is the [[https://datainstitute.univgrenoblealpes.fr/Grenoble Data Science Institute]]. Among its permanent groups and recurrent activities are the [[https://datainstitute.univgrenoblealpes.fr/education/dataclub/Grenoble Data Club]] and [[https://datainstitute.univgrenoblealpes.fr/education/ringrenoble/RinGrenoble]] seminars. 
 
The Data Science track has common courses with the [[http://mosig.imag.fr/MoSIG program]]. The Data Science track is both research and industryoriented. Its purpose is to train highlevel researchers with skills in both the mathematical aspects of Data Science and in practical skills in data analysis and programming.  The Data Science track has common courses with the [[http://mosig.imag.fr/MoSIG program]]. The Data Science track is both research and industryoriented. Its purpose is to train highlevel 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.  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 data science. They are oriented towards acquiring knowledge in machine learning, probabilistic and statistical modelling and optimization.  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 largescale (often meaning highdimensional) aspects of data science. They are dedicated to largescale 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).  Some others focus on largescale (often meaning highdimensional) aspects of data science. They are dedicated to largescale 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 20212022 :** the two couples of courses 
 
 (1) Nonsmooth Convex Optimization Methods + (2) Efficient methods in optimization 
 
 (1) Model exploration for approximation of complex, highdimensional 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 
 
 
* [[lectures#Computational_biologyComputational biology]]  * [[lectures#Computational_biologyComputational biology]] 
* [[lectures#Data_Science_SeminarData science seminar]]  * [[lectures#Data_Science_SeminarData science seminar]] 
* [[lectures#Efficient_methods_in_optimizationEfficient methods in optimization]]  * [[lectures#Efficient_methods_in_optimizationEfficient methods in optimization]], common MSCI 
* [[lectures#Fundamentals_of_probabilistic_data_miningFundamentals of probabilistic data mining]]  * [[lectures#Fundamentals_of_probabilistic_data_miningFundamentals of probabilistic data mining]] 
* [[lectures#GPU_ComputingGPU Computing]], common MSCI  * [[lectures#GPU_ComputingGPU Computing]], common MSCI 
* [[lectures#Information_access_and_retrievalInformation access and retrieval]], common MOSIG  * [[lectures#Information_access_and_retrievalInformation access and retrieval]], common MOSIG 
* [[lectures#Introduction to extremevalue analysisIntroduction to extremevalue analysis]] NEW in 2020  * [[lectures#Introduction to extremevalue analysisIntroduction to extremevalue 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 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 learningKernel methods for machine learning]]  * [[lectures#Kernel methods for machine learningKernel methods for machine learning]] 
* [[lectures#Machine_Learning_FundamentalsMachine Learning Fundamentals]], common MOSIG  * [[lectures#Machine_Learning_FundamentalsMachine Learning Fundamentals]], common MOSIG 
* [[lectures#Model_selection_for_largescale_learningModel selection for largescale learning]]  * [[lectures#Model_selection_for_largescale_learningModel selection for largescale learning]] 
* [[lectures#modelling_seminar_and_projectsModelling Seminar and Projects]]  * [[lectures#modelling_seminar_and_projectsModelling Seminar and Projects]] 
 * [[lectures#Nonsmooth Convex Optimization MethodsNonsmooth Convex Optimization Methods]] NEW in 2021, common MSCI 
* [[lectures#Numerical_optimal_transport_and_geometryNumerical optimal transport and geometry]], common MSCI  * [[lectures#Numerical_optimal_transport_and_geometryNumerical optimal transport and geometry]], common MSCI 
* [[lectures#Reinforcement learningRinforcement learning]], common MOSIG  * [[lectures#Reinforcement learningReinforcement learning]], common MOSIG 
* [[lectures#Software_Development_Tools_and_MethodsSoftware Development Tools and Methods]]  * [[lectures#Software_Development_Tools_and_MethodsSoftware Development Tools and Methods]] 
* [[lectures#Statistical methods for forecastingStatistical methods for forecasting]] NEW in 2020  * [[lectures#Statistical methods for forecastingStatistical methods for forecasting]] NEW in 2020 
 * [[lectures#Temporal and spatial point processesTemporal and spatial point processes]] NEW in 2021 
* [[lectures#Wavelets_and_applicationsWavelets and applications]], common MSCI  * [[lectures#Wavelets_and_applicationsWavelets and applications]], common MSCI 
 