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CM2007 Applied Machine Learning and Data Mining for Performance Analysis 7.5 credits

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For course offering

Spring 2025 Start 14 Jan 2025 programme students

Application code


Headings with content from the Course syllabus CM2007 (Spring 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This course deals with how to process and draw conclusions of data through data mining and machine learning.  The course introduces some theory on machine learning, but focuses mainly on current applied methods.

The following is included in the course:

•Statistical and probabilistic methods for data analysis.

•Different methods for data mining.

•Algorithms for supervised and unsupervised machine learning.

•Neural networks and deep learning.

•Data extraction: purpose and typical use cases in performance analysis.

•Routines for importing, combining, converting and selecting data for learning and validation.

•Validation methods and performance measures.

•Visualization and analysis of results from data analysis.

•Ethics and regulations concerning use and processing of personal data.

Intended learning outcomes

On completion of the course, the student is expected to be able to:

•apply methods to import, combine and convert data into the appropriate format for data analysis, 

•explain the benefits of data mining and be able to choose and implement appropriate methods in typical data mining use cases,

•choose, motivate, and apply standard machine learning methods and algorithms to typical use cases and present the results in appropriate ways

•design and perform performance validation of a machine learning system

•give an account on ethics and regulations when using and processing personal data.

Literature and preparations

Specific prerequisites

At least 10 credits in linear algebra and analysis, at least 6 credits in mathematical statistics, and at least 8 credits in object-oriented programming.

Recommended prerequisites

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Examination and completion

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


  • LAB1 - Laboratory work, 4.0 credits, grading scale: A, B, C, D, E, FX, F
  • RED1 - Reporting, 2.0 credits, grading scale: P, F
  • TEN1 - Oral examination, 1.5 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

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Opportunity to raise an approved grade via renewed examination

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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 room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Technology and Health

Education cycle

Second cycle

Add-on studies

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Jayanth Raghothama (