Foundations of AI and Optimisation Methods

Education facts

Code: DVAE23

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.

Entry 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

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Foundations of AI and Optimisation Methods

  Karlstad University

  Karlstad

  Start date: 31 August 2026

  End date: 8 November 2026

  Pace of study: 50 %