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 -of the human nervous tissue system- which the ANN attempt to simulate. Neural networks regard almost all cases where the relationship between independent and dependent variables is investigated, even if it is highly non-linear and complex, such as predictions, regression, data modeling, feature significance, simulation of functions, partial differential equations, etc. In recent years, researchers and professionals are thoroughly utilizing artificial neural networks, as they have been successfully applied in an unusually wide range of fields of science and technology. Their success stems from the underlying theory of the exact approach of any continuous function in compact fields.
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
- professionals who work on data analytics, predictions, and relevant topics
- researchers which want to analyze scientific databases with contemporary machine learning techniques
- anyone interested to understand the fundamental assumptions and hands-on implementation of machine learning algorithms
- understand the theoretical background 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