Foundations of AI and Optimisation Methods

The course comprises two parts. The first part, which constitutes approximately 60% of the course, treats basic concepts and paradigms of artificial intelligence and machine learning, for instance hypothesis space, generalisation error, and limitations. This first part of the course also includes practical components, such as design of algorithms for linear and logistic regression and support vector machines, and practical aspects of machine learning, for instance normalisation and cross-validation. The second part of the course treats optimisation with and without constraints. Among other things, this includes showing how stochastic gradient descent can be used for optimisation without constraints.

Progressive specialisation: A1F (has second‐cycle course/s as entry requirements)

Education level: Master's level

Admission requirements: Calculus and geometry (7.5 ECTS credits), Calculus in several variables (7.5 ECTS credits), Data structures and algorithms (7.5 ECTS credits), and upper secondary level English 6, 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.

This course is included in the following programme

• Master of Science in Computer Engineering (studied during year 5)
• Master in Computer Science (studied during year 2)