DateTopicLecturer
07th of FebruaryGNU R and its usageSven Laur
I14th of FebruaryDecision trees and association rulesSven Laur
II21th of FebruaryLinear models and polynomial interpolationAnna Leontjeva
III28th of FebruaryPerformance evaluation measuresAnna Leontjeva
IV6th of MarchNumeric optimization methods for continuous functionsKonstantin Tretjakov
V13th of MarchLinear classificationKonstantin Tretjakov
VI20th of MarchFeed-forward neural networks for prediction tasksSven Laur
VII27th of MarchBasics of probabilistic modellingSven Laur
VIII3th of AprilMaximum likelihood and maximum a posteriori estimatesSven Laur
IX10th of AprilModel-based clustering techniquesMeelis Kull
X17th of AprilExpectation-maximisation and data augmentation algorithmsMeelis Kull
XI24th of AprilFactor analysis: PCA, LDA and ICAKristjan Korjus
--1st of MayInternational Workers' DayV.I.Lenin
XII8th of MayStatistical learning theorySven Laur/Jüri Lember
XIII15th of MaySupport Vector MachinesKonstantin Tretjakov
XIV22th of MayOther kernel methodsKonstantin Tretjakov
XV29th of MayBasics of ensemble methodsRaivo Kolde
XVI???Particle filtersRaivo Kolde

Before you submit the solutions

0. GNU R and its usage

Given by Sven Laur
Brief summary: Brief introduction to GNU R. GNU R as a calculator. GNU R as programming language. How to get data into R and how to get data out of R. Data frames and their manipulations. Techniques for computing basic statistics and drawing plots. Loops and if brances.

Slides: PDF

Literature

Home exercises:

  • No home exercises for this exercise session

I. Decision trees and association rules

Given by Sven Laur


Example solutions

II. Linear regression

Given by Anna Leontjeva

Example solutions

III. Performance evaluation measures

Given by Anna Leontjeva

Example solutions with additional comments

IV. Optimization basics

Given by Konstantin Tretyakov

V. Linear classification

Given by Konstantin Tretyakov

VI. Feed-forward neural networks

Given by Sven Laur

Example solutions with additional comments

VII. Basics of probabilistic modelling

Given by Sven Laur

VIII. Maximum likelihood and maximum a posteriori estimates

Given by Sven Laur

IX. K-means and Gaussian mixture models

Given by Meelis Kull

X. Bernoulli mixture models and EM

Given by Meelis Kull

XI. PCA

Given by Kristjan Korjus

XII. Statistical Learning Theory and No Free Lunch Theorems

Given by Sven Laur

XIII. Support Vector Machines

Given by Konstantin Tretyakov

XIV. Kernel methods

Given by Konstantin Tretyakov

XV. Ensemble methods

Given by Raivo Kolde