NDAA09022U Advanced Topics in Data Modelling

Volume 2013/2014
Education
MSc Programme in Computer Science
Content
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
Learning Outcome
At course completion the participant will have obtained

Knowledge in:
  • selected stochastic and deterministic data models and analysis methods and their applications
  • selected data representations.

Skills:
  • Apply static and dynamic data models within appropriate applications.
  • apply selected data representations.
  • Implement selected methods and models.

Competences:
  • 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.
Literature
See Absalon.
Must know the content of either the courses “Statistical Methods for Machine Learning”, “Machine Learning for Pattern Recognition” or similar.

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.
Lectures and project supervision.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 70
  • Project work
  • 108
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Continuous written assignments. Grade: 7-point scale. Internal grading. Submission in Absalon.
Marking scale
7-point grading scale
Censorship form
No external censorship
Re-exam
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.