NDAA09022U Advanced Topics in Data Modelling
The purpose of this course is to introduce the student to
selected advanced topics in stochastic and deterministic modeling
and analysis of real world sampled data. The techniques
taught are widely applicable within computer science,
bioinformatics, and E-Science. Application examples include general
machine learning, image analysis, computer vision and biology. The
course will be evaluated with a small written project.
Foundation of stochastic modeling: The core part of the course covers a selection of topics such as:
• Graphical models
• Learning and inference
• Hidden Markov models (HMM)
• Markov random fields
A selection of image analysis topics will be covered such as:
• Kalman and particle filtering
• Visual tracking and Visual SLAM
• Motion analysis and optical flow
• Texture and image models with applications (texture synthesis and analysis, denoising, inpainting)
A selection of bioinformatics topics will be covered such as:
• Graphical models for structural bioinformatics
• HMMs for biological sequence analysis
• Stochastic context-free grammars for RNA structure
• Probabilistic models for expression analysis a
• Algorithms for large scale genomic mapping
At course completion the participant will have obtained
- selected stochastic and deterministic data models and analysis methods and their applications
- selected data representations.
- Apply static and dynamic data models within appropriate applications.
- apply selected data representations.
- Implement selected methods and models.
- Recognize possible applications of selected stochastic and deterministic data models and analysis methods.
- Contrast and evaluate selected static and dynamic data models
- Writting small scientific report.
Informally, the students are expected to have a mature and operational mathematical knowledge. Knowledge of linear algebra, geometry, basic mathematical analysis, and basic statistics is relevant.
We also expect that the student is able to program in a language suitable for scientific modelling.
- Project work
- 7,5 ECTS
- Type of assessment
- Continuous assessmentThree written assignments. Submission in Absalon.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
- Resubmission of written assignments. Grade: 7-point scale. Internal grading.
Criteria for exam assesment
To obtain the grade 12 the student must be able to:
1. Recognize and describe possible applications of selected stochastic and deterministic data models and analysis methods.
2. Explain, contrast and apply selected data representations.
3. Explain and contrast static and dynamic data models and their applications.
4. Apply static and dynamic data models within appropriate applications.
5. Implement selected methods and models.