The aim of this course is to provide a broad introduction to students on both theoretical as well as practical concepts in machine learning, data mining and pattern recognition.  Topics include fundamental machine learning concepts and algorithms, such as supervised learning (parametric and non-parametric algorithms, classification and regression, discriminative and generative learning), unsupervised learning (clustering, dimensionality reduction, data imputation), and learning theory (bias-variance tradeoff, curse of dimensionality). The course will also include an introduction to deep learning, practical advice for designing machine learning systems, as well as an overview of modern scientific applications of machine learning and data mining (e.g., classification of omics data and applications in biology, object detection and human behaviour analysis, weather forecasting).

Type of methodology: Combination of lecture and hands-on

Participants receive the certificate of attendance: No

Paid training activity for participants: Yes, for all.

Participants prerequisite knowledge: No prerequisite knowledge