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m1_courses [2019/11/15 09:40]
gaudoin [Statistical analysis and document mining (S8)]
m1_courses [2019/11/15 09:41] (current)
gaudoin [Statistical analysis and document mining (S8)]
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 == Content == == Content ==
  
-1. Multiple linear regression. Least squares, Gaussian linear model, test of linear hypotheses, one-way analysis of variance. +  -  ​Multiple linear regression. Least squares, Gaussian linear model, test of linear hypotheses, one-way analysis of variance. 
-2. Principal Components Analysis (PCA). +  ​-  ​Principal Components Analysis (PCA). 
-3. Classification,​ linear discriminant analysis, perceptron, Naive Bayes +  ​-  ​Classification,​ linear discriminant analysis, perceptron, Naive Bayes 
-4. Text mining, numeric representation of texts, connexion with graph clustering.+  ​-  ​Text mining, numeric representation of texts, connexion with graph clustering.
  
 == Prerequisites == == Prerequisites ==
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-== Prerequisites == 
-Elementary notions in probability theory (probability distribution,​ joint probability density function for random vectors, conditional distribution,​ expectation,​ variance, covariance, Gaussian distribution) 
- 
-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.txt · Last modified: 2019/11/15 09:41 by gaudoin
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