Mathematics for Artificial Intelligence II

Education facts

Code: MAGA85

The course consists of two modules that provide prerequisite knowledge in mathematics for technical application courses in machine learning and artificial intelligence. Module 1: Statistics and probability theory (7.5 credits) The module covers the foundations of probability and statistics theory. Key concepts include random experiments, outcomes and events, probability functions, conditional probability, random variables, standard distributions (e.g., Poisson distribution, exponential distribution, and normal distribution), measures of central tendency and dispersion, point and interval estimation, and hypothesis testing for normally distributed populations. Module 2: Optimisation (7.5 credits) The module covers linear and nonlinear optimisation, with and without constraints. Key concepts include Taylor series, necessary and sufficient conditions for optimality, convergence, Newton's method, derivative-free methods, and stochastic gradient descent methods.

Entry requirements

7.5 credits from Mathematics for artificial intelligence I (15.0 credits). An equivalence assessment can be made.

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.

• Mathematics for Artificial Intelligence II

  Karlstad University

  Karlstad

  Start date: 31 August 2026

  End date: 17 January 2027

  Pace of study: 50 %