This course creates a bridge between theoretical knowledge and practical application, opening up a new dimension of thinking. Through this module, participants will develop new mathematical intuitions by contextualizing theoretical questions within practical scenarios. Additionally, the course emphasizes how mathematical skills enhance the understanding and application of machine learning solutions.
The aim of this module is to remodel your mathematical knowledge by shifting the focus from abstract mathematics to mathematics with a specific purpose. This shift equips you for a deeper dive into machine learning, enhancing your ability to interpret results and optimize models.
The course material is organized in Jupyter notebooks using Python as the primary programming language. This setup provides a hands-on approach to learning, allowing you to directly apply mathematical concepts within a practical framework.
This material, in whole or in part, may be:
- Distributed
- Remixed
- Adapted
- Built upon
These activities are permitted for noncommercial purposes only, under the following conditions:
- Attribution: You must provide appropriate credit to the creator, Kinga Sipos. Please link to the online module as a reference.
- ShareAlike: If you remix, adapt, or build upon the material, you must license your contributions under the identical terms.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. For more details, see Creative Commons License.