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* Retrieved from Course syllabus FDD3431 (Spring 2019–)

Content and learning outcomes

Course contents

The course is intended for graduate students in computer science and related fields such as engineering and statistics. The course addresses the question how to enable computers to learn from past experiences. It introduces the field of machine learning describing a variety of learning paradigms, algorithms, theoretical results and applications.

It introduces basic concepts from statistics, artificial intelligence, information theory and control theory insofar they are relevant to machine learning. The following topics in machine learning and computational intelligence are covered in detail

-concept learning

-decision tree learning

-Bayesian learning

-artificial neural networks

-instance based learning

-computational learning theory

-evolutionary algorithms

-reinforcement learning.

Intended learning outcomes

The objective of this course is to give students

·      basic knowledge about the key algorithms and theory that form the foundation of machine learning and computational intelligence

·      a practical knowledge of machine learning algorithms and methods

so that they will be able to

·      understand the principles, advantages, limitations and possible applications of machine learning

·      identify and apply the appropriate machine learning technique to classification, pattern recognition, optimization and decision problems.

Course Disposition

No information inserted

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

To be announced at least 4 weeks before course start at course web page. Previous year: T. Mitchell, Machine Learning, McGrawHill was used.

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, 6,0 hp, betygsskala: 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

Profile picture Örjan Ekeberg

Profile picture Atsuto Maki

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 FDD3431

Offered by

EECS/Robotics, Perception and Learning

Main field of study

No information inserted

Education cycle

Third cycle

Add-on studies

Please discuss with the instructor.

Contact

Atsuto Maki, atsuto@kth.se

Postgraduate course

Postgraduate courses at EECS/Robotics, Perception and Learning