====== XP33PPD – Practical Data Mining Problems (Praktické problémy data mining)  ======
==== Basic info ====
The course is focused on solving practical data mining problems. Lectures
deal with data transformation, pre-processing and verification, selection of
a suitable data mining algorithm and data mining process evaluation and
results interpretation. The attention is paid to solving an individual
data mining problem based on real-life data under the supervision of the
lecturer.

**Where and when:** mid-term and end-term presentations at [[https://www.ciirc.cvut.cz/|CIIRC]]. The room will be announced in the MS Teams channel.

**Teaching:** [[Olga.Stepankova@cvut.cz|prof. RNDr. Olga Štěpánková, CSc.]], [[milan.nemy@cvut.cz|Ing. Milan Nemy]]

**Consultation:** (preferred order)
  - MS Teams
  - E-mail
  - In person at CIIRC



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The course can be completed in two ways (exclusive):

**Option 1** (for students with intermediate knowledge of DM/ML)

//Self-study of DM/ML in R/Python//
  * Read a provided literature (choose one of the following titles)
    * S. V. Burger, Introduction to Machine Learning with R, O’Reilly, 2018.
    * A. C. Müller, S. Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, O'Reilly, 2016.
  * Individual consultation
  * Present 2 topics of your choice (preferably, chapters of the suggested books)

**Option 2** (for students with upper-intermediate to advance knowledge of DM/ML)

//Self-study of two advanced DM/ML topics//
  * Study and present following 2 topics:
  * **Topic 1:** Review of a selected DM/ML topic (midterm). Possible topics:
    * Generative adversarial network
    * (Deep) Reinforcement learning
    * XGBoost
    * Deep Forest: Towards An Alternative to Deep Neural Networks
    * Dimensionality Reduction
    * Frequent Itemsets
    * Mining Spatial Data
    * Review articles at https://distill.pub/
  * **Topic 2:** DM/ML in the context of your dissertation thesis (endterm)