FDD3434 Machine Learning, Advanced Course 7.5 credits

Maskininlärning, avancerad kurs

Show course information based on the chosen semester and course offering:

Offering and execution

No offering selected

Select the semester and course offering above to get information from the correct course syllabus and course offering.

Course information

Content and learning outcomes

Course contents *

Machine learning is the science of algorithms that improve their performance by learning from experience; most often in the form of data with or without labelled examples. Machine learning algorithms are used within a large number of application fields. Independently of the field, a developer of such algorithms need to have a systematic understanding of how a given assignment can be formulated as a machine learning problem. The aim of this course is to give you this systematic understanding. We will present a number of machine learning algorithms and statistical modelling algorithms. But above all, you will learn how the different algorithms are constructed, how they relate to one another and when they are applicable in theory and in practice.

Intended learning outcomes *

After the course, the students should be able to

*explain, derive and implement a number of models of supervised and unsupervised learning,

*analytically demonstrate how different models and algorithms relate to one another,

*explain strengths and weaknesses for different models and algorithms,

*choose appropriate model or strategy for a new machine learning task.

More specifically, regarding methodologies the student should be able to

*explain the EM-algorithm and identify problems where it is applicable,

*explain the terminology for Bayesian networks and topic models and apply these on realistic amounts of data,

*explain and derive boosting algorithms and design new boosting algorithms with different cost functions,

*explain and implement methods for learning of feature representations from various types of data.

Course Disposition

No information inserted

Literature and preparations

Specific prerequisites *

No information inserted

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale *

P, F

Examination *

  • EXA1 - Examination, 7.5 credits, Grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Jens Lagergren

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FDD3434

Offered by

EECS/Computational Science and Technology

Main field of study *

No information inserted

Education cycle *

Third cycle

Add-on studies

No information inserted

Postgraduate course

Postgraduate courses at EECS/Computational Science and Technology