Who is this course for?
This course aims to provide a broad introduction to Bayesian Statistics, and its use in Machine Learning. It is intended for people who have some basic knowledge of statistics and machine learning and want to deepen their knowledge and learn about topics related to parametric and non-parametric Bayesian estimation and inference.
What will you learn from this course?
Students will learn about basic Bayesian statistics concepts and understand the difference to “classical statistics”. They will learn to apply Bayesian methods to real problems and use standard tools for Bayesian analysis and statistical machine learning, including Bayesian deep neural networks.
What is the format for this course?
Instruction type: The course is delivered fully online and in English. It consists of lecture sessions delivered through a video conference tool, followed by a practical lab assignment in Python provided as a Jupyter notebook. There will also be home assignments that the participants need to hand in.
Frequency: The course consists of weekly lectures and assignments.
Examination: Exams will consist of completed labs and exercises. The overall grade of Pass/Fail is awarded for this course.
Email: eric.jarpe [AT] hh [DOT] se
Email: anders.holst [AT] ri [DOT] se