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Data analysis & machine learning

400-1 Data analysis & machine learning Computer Science S7
Lessons : 8 h TD : 6 h TP : 6 h Project : 0 h Total : 20 h
Co-ordinator : Christophe ROSENBERGER
Prerequisite
Probability and statistics (1A)
Course Objectives
The student learns algorithms to extract knowledge from an information dataset, to recognize classes of objects (statistical learning) by learning techniques or to solve problems of statistical modeling by non-conventional techniques (evolutionary algorithms).
Syllabus
- Statistical analysis
- Decision tree
- Bayesian networks
- Neural networks
- Machine learning
- Genetic algorithms
- Boosting
Practical work (TD or TP)
Practical work with Matlab
Titanic Data Analysis
Problem solving with genetic algorithms
Learning with a perceptron for the recognition of heart disease
Acquired skills
Knowledge of Statistical Learning
Knowledge of evolutionary algorithms

Skills block: Mobilize the resources of a scientific and technical field related to a specialty
-> Level 3: Design adaptive systems based on machine learning, deep learning and data mining.
Bibliography
- Gérard Dreyfus , Gérard Dreyfus , Jean-Marc Martinez , Manuel Samuelides "Apprentissage statistique", Edition Eyrolles
- Cornuéjols, A and Miclet L.: Apprentissage Artificiel. Concepts et algorithmes (2nd Ed.with revisions and additions - 2006 Eyrolles, 650 p
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer (2006).

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