Doctoral Course FMI2224

Statistical and Machine Learning

10  Credits
Third Cycle

The course has no offerings planned right now

Learning outcomes for the course

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.