XP33PPD – Practical Data Mining Problems (Praktické problémy data mining)

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 CIIRC. The room will be announced in the MS Teams channel.

Teaching: prof. RNDr. Olga Štěpánková, CSc., Ing. Milan Nemy

Consultation: (preferred order)

1. MS Teams
2. E-mail
3. In person at CIIRC

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)