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m1_courses [2019/10/23 10:25]
picard
m1_courses [2019/11/15 09:41] (current)
gaudoin [Statistical analysis and document mining (S8)]
Line 322: Line 322:
 == Objectives == == Objectives ==
  
-The aim of this course is to present ​advanced statistics and linear modellingvariance analysis ​and provide ​practical ​implementation+The aim of this course is to present ​the statistical approaches for analysing multivariate data. The information age has resulted in masses of multivariate data in many different field: financemarketing, economy, biology, environmental sciences,​...The theoretical ​and practical ​aspects of multivariate data analysis are given equal importance. This balance is achieved through practicals involving actual data analysis using the R software.
  
 == Content == == Content ==
  
-  ​* Principal components ​analysis ​(PCA) +  ​-  Multiple linear regression. Least squares, Gaussian linear model, test of linear hypotheses, one-way ​analysis ​of variance. 
-  ​* Classification (Linear Discr. ​Analysis+  ​-  Principal Components ​Analysis (PCA). 
-  * Data mining ​(text mining+  ​ Classification,​ linear discriminant analysis, perceptron, Naive Bayes 
-  ​* Linear regression +  ​ Text mining, numeric representation of texts, connexion with graph clustering.
-  * Estimation and test of regression parameters +
-  ​* ANOVA +
-  * ANCOVA +
-  * Practical implementation+
  
-This course include practical sessions.+== Prerequisites ==
  
-== Organization == +Elementary notions ​in probability theory ​(probability distribution,​ joint probability density function for random vectors, conditional distribution,​ expectation,​ variance, covariance, Gaussian distribution)
-  - 3ECTS = Lecture 13h + Practical 5h + Lab 15h - Course Joined with Ensimag 2A [[https://​refens.grenoble-inp.fr/​Ensimag/​2015/​modifie?​id=120150000072576&​type=models.Matiere|4MMFDASM]] (head: Jean-Baptiste Durand) +
-  - 3ECTS = Lecture 14h + Lab 6h  - MSIAM specific course (in-depth and practical session) ​(head: Stéphane Girard)+
  
-A short description of the course content can be found {{ :​data-mining-and-multivariate-statistical-analysis-4mmfdas6.pdf |here}}+Elementary notions in mathematical statistics (estimator, confidence interval, ​statistical ​tests)
  
-== Prerequisites ​== +As a bonus: simple linear regression, linear algebra (matrix reductions),​ elementary notions in Rstudio and the R software. 
-Elementary notions ​in probability theory ​(probability distribution,​ joint probability density function for random vectors, conditional distribution,​ expectation,​ variance, covariance, Gaussian distribution)+ 
 +== Organization ​== 
 + 
 +  - 3ECTS = Lecture 16.5h + Practical 7.5h + Lab 9h - Course Joined with Ensimag 2A (head: Jean-Baptiste Durand and Olivier Gaudoin) 
 +  - 3ECTS = Lecture 14h + Lab 6h  - MSIAM specific course (in-depth and practical session) ​(head: Modibo Diabaté)
  
-Elementary notions in mathematical statistics (estimator, confidence interval, statistical tests). As a bonus: simple linear regression. 
  
-Notions in linear algebra (matrix reductions). 
-As a bonus: elementary notions in Rstudio and the R software. 
 ---- ----
 ==== Introduction to Operations Research (S8) ==== ==== Introduction to Operations Research (S8) ====
m1_courses.1571819150.txt.gz · Last modified: 2019/10/23 10:25 by picard
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