Concepts for Machine Learning Practitioners

The course introduces key concepts required to understand fundamental principles in machine learning. It presents the properties that a dataset must have for machine learning to work, and discusses necessary conditions for the underlying distribution of the data. Various types of dependencies and so on are also explored.

The course then examines the impact of loss functions within the context of machine learning and covers popular loss functions and associated algorithms, with particular emphasis on regression and classification.

Finally, the course presents different methods for interpreting the results produced by machine learning algorithms.

Progressive specialisation: A1N (has only first‐cycle course/s as entry requirements)

Education level: Master's level

Admission requirements: Computer Science 60 ECTS credits (including Software Development 15 ECTS credits and Discrete Mathematics 7.5 credits) or three years of work experience in the IT sector, plus upper secondary level English 6 or English level 2, or equivalent

Selection: Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.