Date | Topic | Lecturer | |
---|---|---|---|

0 | 7th of February | GNU R and its usage | Sven Laur |

I | 14th of February | Decision trees and association rules | Sven Laur |

II | 21th of February | Linear models and polynomial interpolation | Anna Leontjeva |

III | 28th of February | Performance evaluation measures | Anna Leontjeva |

IV | 6th of March | Numeric optimization methods for continuous functions | Konstantin Tretjakov |

V | 13th of March | Linear classification | Konstantin Tretjakov |

VI | 20th of March | Feed-forward neural networks for prediction tasks | Sven Laur |

VII | 27th of March | Basics of probabilistic modelling | Sven Laur |

VIII | 3th of April | Maximum likelihood and maximum a posteriori estimates | Sven Laur |

IX | 10th of April | Model-based clustering techniques | Meelis Kull |

X | 17th of April | Expectation-maximisation and data augmentation algorithms | Meelis Kull |

XI | 24th of April | Factor analysis: PCA, LDA and ICA | Kristjan Korjus |

-- | 1st of May | International Workers' Day | V.I.Lenin |

XII | 8th of May | Statistical learning theory | Sven Laur/Jüri Lember |

XIII | 15th of May | Support Vector Machines | Konstantin Tretjakov |

XIV | 22th of May | Other kernel methods | Konstantin Tretjakov |

XV | 29th of May | Basics of ensemble methods | Raivo Kolde |

XVI | ??? | Particle filters | Raivo Kolde |

**Before you submit the solutions**

- Requirements to solutions of home exercises!
- Maximum points for the homework is 10 points. Additional points are not taken into account. They are not bonus tasks!

## 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

- Home exercises to the I session
**Deadline: February 21**

**Example solutions**

## II. Linear regression

**Given by** Anna Leontjeva

- Home exercises to the II session
**Deadline: February 28**

**Example solutions**

## III. Performance evaluation measures

**Given by** Anna Leontjeva

- Home exercises to the III session
**Deadline: March 6**

**Example solutions with additional comments**

## IV. Optimization basics

**Given by** Konstantin Tretyakov

- Home exercises to the IV session
- Sample solutions Δ
**Deadline: March 13**

## V. Linear classification

**Given by** Konstantin Tretyakov

- Home exercises to the V session
- linear_class.R
- Sample solutions Δ
**Deadline: March 20**

## VI. Feed-forward neural networks

**Given by** Sven Laur

- Home exercises to the VI session
- R functions to visualise and manipulate neural networks
- The code written in practice session
**Deadline: March 27**

**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

- Home exercises to the IX session (updated April 13)
- The code written in practice session
- Video: http://uttv.ee/naita?id=10719 (Please log in to the UTTV system with ut account)
- Sample solutions Δ
**Deadline: April 17**

## X. Bernoulli mixture models and EM

**Given by** Meelis Kull

- Home exercises to the X session
- Data input and visualization code to download
- Data file to download
- Another data file to download
- Video: http://uttv.ee/naita?id=10800 (Please log in to the UTTV system with ut account)
- Sample solutions Δ
**Deadline: April 24**

## XII. Statistical Learning Theory and No Free Lunch Theorems

**Given by** Sven Laur

- Home exercises to the XII session
- The code written in practice session and some helper functions
- Video: http://uttv.ee/naita?id=11439 (Please log in to the UTTV system with ut account)
**Deadline: May 15**

## XIII. Support Vector Machines

**Given by** Konstantin Tretyakov

- Home exercises to the XIII session
- svm_base.r
- Video
- Sample solutions Δ
**Deadline: May 22**

## XIV. Kernel methods

**Given by** Konstantin Tretyakov

- Home exercises to the XIV session
- kernel_base.r
- reuters.txt.gz
- Video
- Sample solutions Δ
**Deadline: May 29**

## XV. Ensemble methods

**Given by** Raivo Kolde

- Home exercises to the XV session
- Data for the home exercise
**Deadline: 1 day before exam**