Course contents *

The course is intended for both undergraduate and 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 probability theory in so far they are relevant to machine learning

The following topics in machine learning and computational intelligence are covered in detail

• nearest neighbour classifier
• decision trees
• bias and the trade-off of variance
• regression
• probabilistic methods
• Bayesian learning
• support vector machines
• artificial neural networks
• ensemble methods
• dimensionality reduction
• subspace methods.

Intended learning outcomes *

After passing the course, the student should be able to

• describe the most important algorithms and the theory that constitutes the basis for machine learning and artificial intelligence
• explain the principle for machine learning and how the algorithms and the methods can be used
• discuss advantages with and limitations of machine learning for different applications

in order to be able to identify and apply appropriate machine learning technique for classification, pattern recognition, regression and decision problems.

Course Disposition

No information inserted

Specific prerequisites *

Completed courses in all following subjects:

• the equivalent SF1626 of multivariable analysis
• probability theory and statistics e quivalent SF1912/SF1914/SF1915/SF1916/SF1920/SF1921/SF1922/SF1923/SF1924
• programming equivalent DD1337/DD1310/DD1312
• algorithms and data structures equivalent DD1338/DD1320/DD1321/DD1325.

The above requirements are the specific entry requirements to the Master's programme (two-year) in machine learning.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course. This applies only to students who are first-time registered for the prerequisite course offering or have both that and the applied for course offering in their individual study plan.

Recommended prerequisites

No information inserted

Equipment

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Literature

No information inserted

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

Grading scale *

A, B, C, D, E, FX, F

Examination *

• LAB1 - Laboratory Work, 3.5 credits, Grading scale: P, F
• TEN1 - Examination, 4.0 credits, Grading scale: A, B, C, D, E, FX, 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.

The exam  is written.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

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.

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.

Offered by

EECS/Intelligent Systems

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Atsuto Maki (atsuto@kth.se)

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex