NDAK15014U  Advanced Topics in Machine Learning (ATML)

Volume 2017/2018
Education

MSc Programme in Computer Science

MSc Programme in Statistics

Content

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:

  • PAC-Bayesian analysis

    • 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

  • Deep Learning

    • Another highly successful and hugely popular learning approach

  • Online learning

    • How to learn when data collection and learning are coupled together

    • How to adapt to changing and adversarial environments

  • Reinforcement learning

    • 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.

Learning Outcome

Knowledge of

  • 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.

The course requires strong mathematical background. It is suitable for computer science master students, as well as students from mathematics (statistics, actuarial math, math-economics, etc) and physics study programs. Students from other study programs are strongly advised to contact the course responsible and verify suitability of their background prior to signing up for the course.

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.)
Lectures and class instructions.
Credit
7,5 ECTS
Type of assessment
Continuous assessment
5-7 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

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.

 

  • Category
  • Hours
  • Lectures
  • 28
  • Class Instruction
  • 14
  • Preparation
  • 70
  • Exercises
  • 74
  • Exam Preparation
  • 20
  • Total
  • 206