dr hab. inż. Wojciech Kotłowski (wkotlowski cs put poznan pl)

dr hab. inż. Krzysztof Dembczyński (kdembczynski cs put poznan pl)

17-05-2019 |
Change of lecture dates! The last lecture will be held on May 30. |

25-04-2019 |
The course on decision-theoretic machine learning has finally begun :) |

**The aim of the course:**

To explain theoretical foundations of machine learning in order to show how simple algorithms can be used for solving complex problems

**The scope of the course:**

- Introduction to applications and theory of machine learning,
- An overview of the basic learning algorithms (lazy learning, decision trees, generative models, discriminative models, linear models),
- Learning theory (loss function, risk, regret, empirical risk minimization, generalization bounds, calibration),
- Learning to rank (performance measures, learning theory for ranking problems, reduction to binary classification),
- Multi-label classification (performance measures, learning theory for multi-label classification, learning reductions for multi-label classification).

- Lectures: April 25, May 9, 16 and 30, 15:10-18:20, lecture room 122 BT

25-04-2019 | Introduction to the course [pdf] |

25-04-2019 | Machine learning [pdf] |

09-05-2019 | Binary classification [pdf] |

16-05-2019 | Bipartite ranking [pdf] |

30-05-2019 | Multi-label classification [pdf] |

In order to pass the course you need to solve some of the problems described in the pdf below from 4 different topics. For each solved problem you can get max. 1 point. However, you cannot get more than 1 point from a given topic. The final mark will be given according to the following rule:

- 3.0 points - 5.0
- 2.0 points - 4.0
- 1.0 points - 3.0

Your solutions should be sent (in a LaTeX-generated PDF file) to both instructors via email. Please use a tag ‘[DTML]’ in the title.

The deadline is **June 30, 2019**.

Description of problems: [pdf]

T. Hastie, R. Tibshirani, J. Friedman, Elements of Statistical Learning: Second Edition. Springer, 2009.

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Y. S. Abu-Mostafa, M. Magdon-Ismail, H-T. Lin, Learning From Data.

http://amlbook.com

D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

http://www.cs.ucl.ac.uk/staff/d.barber/brml/

Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2006.