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 insofar 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 *

The aim of the course is to give the students

• basic knowledge of the most important 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

• explain the principles, advantages, limitations such as overfitting 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

Specific prerequisites *

For independent course students, 90 credits are required of which 45 credits in mathematics, informatics and/or SF1604 Linear Algebra as well as the courses SF1625 One variable calculus, SF1626 Multivariable analysis, SF1901 Mathematical Statistics, DD1337 Programming and DD1338 Algorithms and Data Structures or the equivalent.

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

Is announced on the course web page before start of the course.

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.

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

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)

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.

Supplementary information

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