Information processing

Objectives:
Acquisition of basic concepts and tools for data processing:
- Quantitative data (numerical) : Principal Component Analysis (PCA)
- Qualitative data (categories): Correspondence Factorial Analysis (CFA)
- Introduction to machine learning: unsupervised (clustering) and supervised (classification, regression) learning

Skills:
- Know the basic principles of data processing and machine learning (Quiz)
- Know how to implement a simple data analysis method (Practical work)
- Know how to analyze the results of an analysis method applied to a database (Project)

- Basics of linear algebra (matrix/vector manipulation, matrix diagonalization), statistics (mean, median, variance) and Python.

Introduction to multidimensional data processing methods, Correspondence Factor Analysis, Principal Component Analysis, Classification.
Programming language used: python.

Course slides