Development and principles of Machine Learning. Machine Learning Techniques, supervised/ unsupervised learning, Baeysian networks, Fuzzy logic, Decision Trees, Support Vector Machine, Artificial neural networks, Deep learning, Dataset selection and development, Normalisation, ML development with the use of Python, R and Matlab, Performance assessment of ML models, Overfitting issues, ML cases studies in mining and metallurgical applications.
