Bayesian Statistics for Machine Learning

The course is broken down into:

• Basic Bayesian concepts
• Selecting priors, deriving some equations
• Bayesian inference, Parametric model estimation
• Sampling based methods
• Sequential inference (Kalman filters, particle filters)
• Approximate inference, variational inference
• Model selection (missing data)
• Bayesian deep neural networks

Entry requirements

Degree of Bachelor of Science with a major in Computer Science and Engineering or Degree of Bachelor of Science in Engineering, Computer Science and Engineering or the equivalent of 180 Swedish credit points or 180 ECTS credits at an accredited university. Including 5 credits statistics and 5 credits machine learning. Applicants must have written and verbal command of the English language equivalent to English course 6 in Swedish Upper-Secondary School.