Course contents *
â€¢ The logistic and experiences of a machine learning student at KTH: courses tracks and thesis project.
â€¢ Where do machine learning graduates work? Academia, industry and the public sector
â€¢ The ethics of making conclusions from experiments and results and presenting these to the public.
â€¢ Privacy, security and ethical issues around "big data".
â€¢ What machine learning can and cannot predict
â€¢ Conduct code for a machine learning scientists
Intended learning outcomes *
As student at KTH:
To pass the course, the students should
â€¢ have been informed about course choices they made during their studies and reflected over why they made them,
â€¢ have been informed about the expected content of the masters degree project and the opportunities available to them
To pass in the course, the student should
â€¢ be aware of 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
â€¢ be aware of the responsibilities when presenting machine learning results/algorithms to the public,
â€¢ be aware of the responsibilities of drawing conclusions from experimental results.
As a future professional machine learning scientist
To pass the course, the student should
â€¢ be aware about how machine learning is used and be utilised outside the academic world and the consequences this has for the society and the professional responsibilities as a machine learning practitioners.
â€¢ be more aware of workplaces and professions available for them as machine learning graduates.