Topic outline

  • Join lectures and exercises: Zoom and Slack

    LecturerPhD Markus Heinonen
    Co-lecturers: Prof Arno Solin, Prof Harri Lähdesmäki, Prof Aki Vehtari, Phd Vincent Adam, PhD William Wilkinson, PhD Charles Gadd
    Teaching assistants: Paul Chang, Phd Martin Trapp, Pashupati Hegde

    Gaussian processes (GPs) are a powerful tool for Bayesian nonparametric modelling. This course will give an overview of Gaussian processes in machine learning, and provide a theoretical background. The course will include Gaussian process regression, classification, unsupervised modelling, as well as deep GPs and other more complex and recent advances.

    Target audience:
    The course is targeted towards Msc students interested in machine learning:


    Basics of machine learning and statistics, eg. Machine Learning: Supervised methods (CS-E4710)

    The 5 credit course will contain 11 lectures, 5 weekly practical assignments and optional project work for 2 extra credits. The practical assignments will be based on Python. Other languages (such as Matlab and R) can be used, but it will require more work from the participants. Whole course is online.

    Exam: no exam

    Grading: max 20 points
    Five assignments, each worth 3 points (max 15 points). 
    Extra point for participation (choose one) in a weekly excercise session (max 5 points)
    • H1: wednesdays 12:15-14:00 
    • H2: fridays 12:15-14:00

    Book: Gaussian processes for Machine learning, MIT Press 2006 (publicly available)

    Session #1: monday January 11th, 12:15-14:00
    Introduction to Gaussian distribution and Bayesian inference

    Session #2: thursday January 14th, 10:15-12:00
    Bayesian regression over parameters and functions

    Session #3: monday January 18th, 12:15-14:00
    Gaussian process regression, kernels, computational complexity

    Session #4: thursday January 21th, 10:15-12:00
    Gaussian process classification, introduction to variational inference

    Session #5: monday January 25th, 12:15-14:00
    Latent modelling for unsupervised and supervised learning

    Session #6: thursday January 28th, 10:15-24:00
    Kernel learning

    Session #7: monday February 1st, 12:15-14:00
    Convolution GPs 

    Session #8: thursday February 4th, 10:15-12:00
    Deep Gaussian processes

    Session #9: monday February 8th, 12:15-14:00
    Model selection

    Session #10: thursday February 11th, 10:15-12:00
    State-space Gaussian processes

    Session #11: monday February 15th, 12:15-14:00
    Dynamical models