This course aims to introduce students to Pattern recognition scientific discipline whose goal is the classification of objects into a number of categories or classes. These objects can be images or signal waveforms or any typeof measurements that need to be classified. Topics that will be covered include:, Nearest neighbor approach (KNN), Decision trees and Random Forest, Bayes decision theory and probability essentials, Gaussian classifier, Gaussian mixture models and clustering (including K-means), Hidden Markov models (and speech recognition basics), Linear classifiers , Multilayer perceptron neural network, Support vector machines
- Teacher: Manal Helal