NDAK15014U Advanced Topics in Machine Learning (ATML)
The purpose of this course is to expose the student to selected advanced topics in machine learning. The course will bring the student up to a level sufficient for master thesis work within machine learning.
Knowledge on
- Selected advanced topics in machine learning, including:
- design of learning algorithms
- analysis of learning algorithms
The exact list of topics will depend on the teachers and trends in machine learning research. They will be announced on the course's Absalon website.
Skills to
- Read and understand recent scientific literature in the field of machine learning
- Apply the knowledge obtained by reading scientific papers
- Compare machine learning methods and assess their
potentials and shortcommings
Competences to
- Understand advanced methods, and to transfer the gained knowledge to solutions to practical problems
- Plan and carry out self-learning
See Absalon.
- Category
- Hours
- Class Instruction
- 14
- Exam Preparation
- 20
- Exercises
- 74
- Lectures
- 28
- Preparation
- 70
- Total
- 206
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessment5-7 weekly take home exercises.
The final grade will be the average over all assignments except the worst one. - Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
30 minutes oral exam in course curriculum, without preparation.
To be eligible for the re-exam, a student must have handed in all but at most two assignments, each demonstrating serious efforts to solve the assignment.
Criteria for exam assesment
To obtain the grade 12 the student must be able to:
1. Document understanding of the assignments
including the relevant literature and/or other materials needed for
conducting the assignment.
2. Document solutions to the assignment.
3. Document any experiments made and any drawn
conclusions.
Course information
- Language
- English
- Course code
- NDAK15014U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Yevgeny Seldin (6-7b6d746c7176486c7136737d366c73)