**Upcoming information**

24/Nov.-22/Dec., 20 hours, 4h/week, every Friday, 18:00-22:00, 300€, Location: web platform.

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. Thus, an artificial neural network is an abstract algorithmic construction, which falls within the area of computational intelligence, aiming to achieve computational simulation of the operation of biological neural networks based on rigor mathematical models, assuming layers of nodes, which are the building blocks of the network. There are three types of nodes: input, output and computational (hiddenTopics

**Topics**

__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

**Who should attend / course benefits**

- professionals who work on data analytics, predictions, and relevant topics
- researchers which want to analyze scientific databases with modern machine learning techniques
- anyone interested to understand the fundamental assumptions and hands-on implementation of machine learning algorithms

**Course benefits**

- understand the fundamental assumptions of machine learning
- hands-on experience, with industrial and academic test cases
- transition from explicit statistics to non-linear, generalized models

Course instructor: Dr. Nikolaos Bakas