Topic outline

  • General

    Gaussian processes (GPs) are a powerful machine learning paradigm for Bayesian nonparametric modelling. This course will give an overview of Gaussian processes in machine learning, and it provides both a theoretical and practical background for leveraging them. The course covers Gaussian process regression, classification, and unsupervised modelling, as well as a selection of more recent specialised topics.

    Lecturer(s): Prof. Arno Solin and Dr. Ti John
    Visiting lectures given by Prof. Aki Vehtari and Dr. Aidan Scannell

    Teaching assistants: Aleksanteri Sladek, Severi Rissanen, Nazaal Ibrahim, Prakhar Verma

    Prerequisites: Basics of machine learning and statistics, e.g. "Machine Learning: Supervised Methods (CS-E4710)".

    Target audience: The course is targeted towards M.Sc. students interested in deepening their machine learning knowledge:
    • GPs are a probabilistic counterpart of Kernel Methods (CS-E4830)
    • GPs offer a probabilistic way to do Deep Learning (CS-E4890)
    • GPs fall under the umbrella of Bayesian Data Analysis (CS-E5710)
    • GPs utilize Advanced Probabilistic Methods (CS-E4820)

    Assignment Deadlines:

    • Assignment 1: March 7th, 10:15 AM
    • Assignment 2: March 14th, 10:15 AM
    • Assignment 3: March 21st, 10:15 AM
    • Assignment 4: April 4th, 10:15 AM
    • Assignment 5: April 11th, 10:15 AM
    • Assignment 6: April 18th, 10:15 AM