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.
If you are interested in courses we offer at the doctoral level, please contact Märet Brunnstedt, Coordinator, Doctoral Studies.
