Programme

Course Objectives: The aim of this course is to use basic and widely applied methods for data modelling and machine learning, and to gain practice by applying machine learning applications to business problems in modelling real world data. Participants will gain the knowledge, skills and competence to apply, analyse and interpret correct machine learning algorithms in business problems with any software tool.

Course Content: This course covers methods and applications of statistical and machine learning algorithms for prediction, classification, clustering, association rules, visualisation, dimension reduction and rule mining. Participants will be introduced to methodologies, technologies and algorithms for machine learning. Topics include supervised (Classification and Regression Task) and unsupervised learning (clustering, association rules and dimensionality reduction). All course materials prepared by the instructor will be available. KNIME (The Konstanz Information Miner), WEKA, R programme, IBM SPSS Modeler and Python programmes are used in course applications.

Course Objectives: The aim of this course is to introduce the use of convolutional neural networks (CNN) showing some real data based exemples in livestock production.

Course Content: This course covers methods and applications of CNN in precision livestock farming. Participants will be introduced to methodologies, technologies and algorithms for CNN. Sessions will be devoted to introduce CNN, Computer exercises about the use of CNN  for classification of equine pain face and for pig counting and monitoring. All course materials prepared by the instructor will be available. R, R-Studio programme and  Keras and Tensorflow for R are used in course applications.