Select the semester and course offering above to get information from the correct course syllabus and course offering.
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.
The aim of the course is to give the students
so that they will be able to
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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.
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Is announced on the course web page before start of the course.
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.
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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 DD2421Computer Science and Engineering
Second cycle
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Atsuto Maki (atsuto@kth.se)
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex