After completing this course successfully, the student can
- Describe and compare advanced machine learning methods and techniques. Methods and techniques include: Graphical models, Dynamical models, Deep Learning, Boosting and Committees of classifiers.
- Design, implement and systematically evaluate advanced machine learning methods and techniques.
- Apply advanced Machine Learning methods and techniques in a realistic case (taken from research projects) in the area of Human Media Interaction such as Human Behavior, Understanding, Social Intelligent Computing and Brain Computer Interfacing.
This second part of the Machine Learning (ML) course is geared towards the knowledge of and the ability to apply advanced ML models and techniques, especially Graphical models, Dynamical Models, Deep Learning and Learning Committees of Classifiers, such as Random Forest, as these techniques are particularly important in the field of Human Media Interaction (HMI). In the final part of the course students will work in groups on a realistic case (taken from actual research projects) and will have to work methodologically through the whole procedure from cleaning up data to the final model selection and performance evaluation. The emphasis of these projects will be on applications of ML in HMI related topics such as Human Behavior Understanding, Social Intelligent Computing and Brain Computer Interfacing. This course has no final exam: the grade will be based on assignments, progress presentations and the report of the project. |
Machine Learning I course.
Graphical Models, Dynamical Models, Deep Learning, Random Forest, Committees of Classifiers.