Machine Learning Form


Artificial Intelligence and Machine Learning algorithms are involved in a vast variety of scientific and industrial projects, contributing to the solution of the emerging problems, from data modeling and analysis to face recognition and self-driving cars. Regardless the practical utilization of machine learning algorithms, their core problem is the construction and training of an Artificial Neural Network, which is inspired from biological neurons -from a human nervous tissue system- which the ANN attempt to simulate.


Supervised Learning: Fundamentals of function approximation.Interpolation methods. Nonlinear regression.Logistic regression. Neural Networks as universal approximators. Training Algorithms. Train & Test set. Overfitting & Overtraining. Ensemble models (Cross validated, Random Forests etc.). Foreward and Backward Stepwise models. Radial basis kernel approximators, Classification algorithms. Unsupervised Learning: Multidimensional scaling. Hierarchical Trees. Clustering (k-Means, linkage etc). Visualization and mapping. Quantification of features importance. Relieff. Connection weights. Partial derivatives. Sensitivity analysis. Input perturbation. Relieff feature selection. Forward & Backward stepwise addition.

Course Instructor: Dr. Nikolaos Bakas

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