NDAK15014U Advanced Topics in Machine Learning (ATML)
MSc Programme in Computer Science
MSc Programme in Statistics
The purpose of this course is to expose students to selected advanced topics in machine learning. The course will bring the students up to a level sufficient for writting their master thesis in machine learning.
The course is relevant for computer science students as well as students from other studies with good mathematical background, including Statistics, Actuarial Mathematics, Mathematics-Economics, Physics, etc.
Examples of topics that were taught in the last year include:
The soul of Machine Learning: How to trade-off prior knowledge and data fit in a principled way
Advanced topics on Support Vector Machines
Learn fast ways to train one of the most successful learning models – Support Vector Machines
Another highly successful and hugely popular learning approach
How to learn when data collection and learning are coupled together
How to adapt to changing and adversarial environments
How to learn when agent actions are changing its state
** The exact list of topics in the current year will depend on the lecturers and trends in machine learning research and will be announced on the course Absalon website. Feel free to contact the course responsible for details.
- 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.
- 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
- Understand advanced methods, and to transfer the gained knowledge to solutions to practical problems
- Plan and carry out self-learning
It is assumed that the students have successfully passed the “Machine Learning” course. In case you have not taken the “Machine Learning” course, please, contact the course responsible to obtain the relevant material and do the necessary self-preparation before the beginning of the course. (For students with strong mathematical background and some background in machine learning it should be possible to do the self-preparation within a couple of weeks.)
- 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.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
The re-exam consists of two parts. The two parts will be given an overall assessment.
The first part is handing in at least 5 of the course assignments no later than 2 weeks before the oral part of the exam.
The second part is an oral exam (30 minutes) in the course curriculum without preparation.
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.
- Class Instruction
- Exam Preparation