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m2courses [2021/04/29 10:20]
etore [Data Science (DS)]
m2courses [2021/08/31 13:55] (current)
picard
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   * */   * */
  
-====== Tracks in semester 9: 2020-2021  ======+====== Tracks in semester 9: 2021-2022  ======
  
 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_m2-msci_s9-2021-22.pdf |here}} 
-  * [[m2courses#Data_Science_(DS)|Data Science]]+  * [[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).  However a personalized track may also be build for some students from the available courses (if no timetable conflicts appears). 
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   * 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]].   * 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: 2019-2020  (2020-2021: upcoming ...)======+====== Semester 10: 2020-2021  ======
 [[aboutmasterproject|MSc thesis: rules, advice for guidance, documents and schedule of defences]] [[aboutmasterproject|MSc thesis: rules, advice for guidance, documents and schedule of defences]]
  
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 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 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 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 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.  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 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). 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
  
  
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   * [[lectures#Non-smooth Convex Optimization Methods|Non-smooth Convex Optimization Methods]] NEW in 2021, common MSCI   * [[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#Numerical_optimal_transport_and_geometry|Numerical optimal transport and geometry]], common MSCI 
-  * [[lectures#Reinforcement learning|Rinforcement learning]], common MOSIG+  * [[lectures#Reinforcement learning|Reinforcement learning]], common MOSIG
   * [[lectures#Software_Development_Tools_and_Methods|Software Development Tools and Methods]]   * [[lectures#Software_Development_Tools_and_Methods|Software Development Tools and Methods]]
   * [[lectures#Statistical methods for forecasting|Statistical methods for forecasting]] NEW in 2020   * [[lectures#Statistical methods for forecasting|Statistical methods for forecasting]] NEW in 2020
m2courses.1619684454.txt.gz · Last modified: 2021/04/29 10:20 by etore
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