Upon completion of the course, the PhD-student shall be able to:
• Select a suitable statistical models, and methods for a data analysis problem in the real world based on reasoned argument, especially when the underlying data generating mechanism is unknown. • Apply various supervised and unsupervised statistical learning algorithms in a range of real world problems. • Evaluate and optimise the performances of the learning models and algorithms, and communicate the expected accuracy of the model/algorithm. • Combine several models to achieve higher predictive accuracy. • Apply Neural Networks to real world problem solving. • Conduct comparative analysis, both theoretical and empirical, in order to decide which Neural Network is most suitable for a particular task. • Design different kinds of Neural Network, evaluate their performance, and use them to solve complex problems. • Apply deep learning to real world problems.