Fundamentals in statistical pattern recognition

This course will cover the broad regression, classification and probability distribution modeling methods and more particularly: Linear regression, Logistic regression, k-NN, Decision Trees, Boosting, Dimensionality reduction (PCA, LDA, t-SNE), k-Means, GMMs, MLPs, CNNs, SVMs.

A - Introduction

• Data representation,
• Pattern Recognition and Machine Learning,
• Lab preparation (JupyterHub, Python and pyTorch).

B - Regression and Classification

• Linear Regression,
• Logistic Regression and Regularization, Overfitting and Capacity,
• k-NN, Decision Trees,
• Artificial Neural Networks: Multi-Layer Perceptron (MLP) and Back-Propagation
• Deep Learning : Convolutional Neural Networks (CNN) and Optimization
• Support Vector Machines

C - Dimensionality reduction and Clustering

• Principal Component Analysis (PCA),
• Linear Discriminant Analysis (LDA),
• k-Means, Single Linkage,
• t-SNE.

D - Probability distribution modelling

• Gaussian Mixture Models (GMM) and the Expectation-Maximization (EM).