NMAK17008U Sparse Learning
MSc programme in Statistics
A sparse statistical model is one having only a small number of nonzero parameters. Examples of models and problems that will be considered in the course are: regression models, matrix decompositions and Gaussian graphical models.
The theme of the course is estimation and statistical inference with sparsity inducing penalties. Lasso and its variations are the a main examples. Sparse estimation is often achived via convex optimization, and this theory will also be treated in the course.
In the course there will be a focus on models and algorithms, and how to apply them to real problems. Some results from the statistical theory will be touched upon, but it will not be a main part of the course.
Knowledge:
Skills: Ability to
Competences: Ability to
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See Absalon for a list of course literature.
- Category
- Hours
- Course Preparation
- 119
- Exam
- 45
- Lectures
- 28
- Theory exercises
- 14
- Total
- 206
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- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentThe course evaluation consists of three individual assignments, which each count 1/3 to the total grade.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Re-exam
Same as the ordinary exam.
If the ordinary exam is not passed, it is possible to hand in one or more of the three assignments in an improved version. The grade is based on all three assignments – equally weighted.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK17008U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- C
- Course capacity
- No restrictions/no limitation
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course Coordinators
- Niels Richard Hansen (14-716c686f763175316b6471766871437064776b316e7831676e)