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DD2301 Program Integrating Course in Machine Learning 3.0 credits

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For course offering

Autumn 2024 prosamML23 programme students

Application code


Headings with content from the Course syllabus DD2301 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

• The logistic and experiences of a machine learning student at KTH: courses, tracks and degree project.

• Where do machine learning graduates work? academia, industry and public sector.

• The ethics of making conclusions from experiments and results and presenting these to the public.

• Privacy, security and ethical issues around "big data".

• What machine learning can and cannot predict.

• Code of conduct for machine learning scientists.

Intended learning outcomes

After passing the course, the student shall be able to

  • reflect on choices and possibilities in the studies;
  • reflect on the ethical issues that are associated with "big data" and the choices about the gains and losses made when mass data about people is made available;
  • reflect on the responsibilities when presenting machine learning results/algorithms to the public;
  • reflect in a deeper way over the value of diversity and equal opportunities between the sexes in the research domain machine learning on companies, departments, and in society;
  • Explain how machine learning is used  outside the academic world and the consequences this has for the society and the professional responsibilities as a machine learning practitioners;
  • give an account of workplaces and professions available for graduate in machine learning;

in order to

  • be able to be a good student;
  • be able to make ethical considerations in the working life;
  • become a professional expert in the area of machine learning.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

No information inserted


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No information inserted

Examination and completion

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

Grading scale

P, F


  • UPP1 - Homework Assignments and Seminar Participation, Year 1, 1.5 credits, grading scale: P, F
  • UPP2 - Homework Assignments and Seminar Participation, Year 2, 1.5 credits, grading scale: P, 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


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

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted


Josephine Sullivan

Supplementary information

In this course, the EECS code of honor applies, see: