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Machine Learning


-       Introduction to machine learning

-       Optimization of the bias / variance balance

-       PLS regression, Linear and quadratic parametric discriminant analysis, k nearest neighbors.

-       Neural networks, multilayer perceptron, introduction to deep learning.

-       Classification and regression trees

-       Bagging, random forests, gradient boosting

-       Missing data imputation

-       Outlier detection and one class classification

-       Scientific deontology and statistical decision


At the end of this module, the student will have understood and be able to explain (main concepts):


-       Properties and limits of principal machine learning methods.

-       Bias - variance balance.

-       Algorithms of risk estimation – bootstrap, cross validation.

-       Optimization and algorithmic implementations in Python (Scikit-learn) of principal methods.


The student will be able to:

-       Analyse big data from different domains: insurance, marketing, industry, by using Python librairies.

-       Execute principal machine learning methods and algorithms (PLS, discriminant analysis, k-nn, classification and regression trees, neural networks, boosting, random forest, SVM...)

-       Optimize hyper-parameters values et construct python pipelines for automatization.

Recommended prerequisite

Statistical modelling

Exploratory Data Analysis

R and Python languages

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...