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    Machine Learning practice session (1.04)

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    Klipi teostus: Mirjam Paales, Sven Laur 01.04.2013 3452 vaatamist Arvutiteadus


    VIII. Basics of probabilistic modelling

    Given by Sven Laur
    Home exercises to the VIII session
    Materials for the exercise session

    Deadline: April 8

    Seotud videod

    Machine Learning - Ia. Introduction to the course
    12.02.13
    Machine Learning - practice session (18.02)
    18.02.13
    Machine Learning - Linear models and polynomial interpolation
    19.02.13
    Machine Learning - practice session (25.02)
    25.02.13
    Machine Learning - Performance evaluation measures
    26.02.13
    Machine Learning - Introduction to optimization
    05.03.13
    Machine Learning - practice session (11.03)
    11.03.13
    Machine Learning - Linear Classification
    12.03.13
    Machine Learning - practice session (18.03)
    18.03.13
    Machine Learning - Feed-forward neural networks for prediction tasks
    19.03.13
    Machine Learning - practice session (25.03)
    25.03.13
    Machine Learning - Basics of probabilistic modelling
    26.03.13
    Machine Learning - Maximum likelihood and maximum a posteriori estimates
    02.04.13
    Machine Learning practice session (8.04)
    08.04.13
    Machine Learning - Model-based clustering techniques
    09.04.13
    Machine Learning - practice session (15.04)
    15.04.13
    Machine Learning - Expectation-maximisation and data augmentation algorithms
    16.04.13
    Machine Learning practice session (22.04)
    22.04.13
    Machine Learning - Principal Component Analysis
    23.04.13
    Machine Learning practice session (29.04)
    29.04.13
    Machine Learning - Statistical learning theory
    30.04.13
    Machine Learning practice session (6.05)
    06.05.13
    Machine Learning - Support Vector Machines
    07.05.13
    Machine Learning - Kernel Methods
    14.05.13
    Machine Learning practice session (21.05)
    20.05.13
    Machine Learning - Basics of ensemble methods
    21.05.13
    Machine Learning - Particle filters
    28.05.13