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Content and learning outcomes
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
- 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.
Literature and preparations
Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.
Knowledge in linear algebra, 7,5 credits, equivalent to completed course SF1624/SF1672/SF1684.
Knowledge in multivariable calculus, 7,5 credits, equivalent to completed course SF1626/SF1674.
Knowledge in probability theory and statistics, 7,5 credits, equivalent to completed course SF1910-SF1924/SF1935.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- 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
Opportunity to raise an approved grade via renewed examination
- 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 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 DD2421
Main field of study
In this course, the EECS code of honor applies, see:
The courses DD1420 and DD2421 overlap with regard to their contents. One can not recieve credit for both courses.