EN_EP013M62_CHARBONNIER_Apprentissage_Et_Reconnaissance_De_Formes

LEARNING AND PATTERN RECOGNITION |

Main lecturer Mail address Phone number | Pierre Charbonnier, Directeur de Recherche MEDDE +33 (0)3 68 77 46 44 | |

Other instructor(s) | “N/A” |

APOGEE code Track - Year - Option - Semester Coefficient = ECTS Duration | EP013M62 Engineer - 3Y G ISSD - S9 Master - 2Y ID G + HCI / TIS / Topo - S3 2 / 4,5 / 2 (Topo) 14h CM, 17,50h TP(Computer Exercises) |

EXAMS Duration Authorized documents If yes, which ones : School calculator authorized | Session 1 1h30 Yes lecture notes Matlab | Session 2 1h30 Yes lecture notes Matlab |

Prerequisites Basic knowledge in statistics, probabilities and image processing. | ||

Lecture goals The goals of this lecture are to introduce the main statistical and neuromimetic approaches of classification and pattern recognition. | ||

Detailed outline - Introduction
- Statistical approaches: Bayesian decision theory, the Gaussian case,
quadratic and linear discriminant functions - Probability density estimation, parametric methods, non-parametric methods : Parzen windows, k-nearest neighbours
- Dimensionality reduction techniques: feature extraction using Principal Component Analysis, Fisher linear discriminant analysis. Feature selection methods
- Unsupervised classification, or clustering: mixture densities and the EM algorithm, the k-means algorithm, hierarchical clustering techniques
- Linear and non-linear discriminant functions: neural approaches, the multi-layer Perceptron,
Radial Basis Functions, Support Vector Machines - Ensemble learning, decision trees, random forests, AdaBoost
- Evaluation of classifiers
- Feature engineering vs. feature learning
- Conclusion
| ||

Applications - 1D Bayesian classification
- Parametric density estimation, likelihood tests
- Naive Bayes classifier
- Non-parametric probability density estimation methods (Parzen, k-NN), Bayesian classifiers based on NP density estimators
- Principal Component Analysis, application in pattern recognition
- Fisher linear discriminant and supervised classification
- Unsupervised classification using the k-means algorithm
- Linear discriminants (least squares and logistic regression)
| ||

Acquired skills After this course, the student will know the basics of the main classification and pattern recognition algorithms and will be able to apply them. |