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Content and learning outcomes
• 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
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- 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
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 DD2301
Main field of study
In this course, the EECS code of honor applies, see: