![Przemysław Biecek <br/> [p-sh-eh-m-eh-k]](https://pbiecek.github.io/images/profile.png)
Published:
An academic textbook describing estimation and testing topics for linear models with fixed effects, random effects and mixed effects. The theoretical introduction is complemented by numerous examples for one-way and multivariate ANOVA, one and multiple random components. The examples focus on biological and medical applications and are based on real analyses of real data.
Published:
Discover! Reveal! Explain! These three roles can be fulfilled by good statistical graphics. Good means understandable, faithful to the data, aesthetic. How to create such graphics? A collection of essays on the art of displaying data systematises knowledge useful in designing and producing good data visualisations. It is not easy. On the one hand, we can fall into the trap of a colourful mush full of numbers, which is sometimes proudly called infographics. On the other hand, we can fall into the trap of graphics that perfectly reproduce the complexity of numbers, and thus completely incomprehensible. Somewhere in the middle is a graphic that explains, that informs, that is aesthetically pleasing and informative.
Published:
The Guide to the R package was the first published Polish book focused on the R language. The current fourth edition consists of four parts: Basics of using R (+tidyverse, shiny, knitr and other goodies), Programming in R (object-oriented, package development, class system), Statistics with R (statistical tests, models, exploration techniques) and Visualization with R (graphics, lattice and ggplot2 packages).
Published:
Are you curious about fractals? The Chaos Game is the book for you. You will learn the mathematical basis behind these figures, find out what algorithm can be used to code them, write code in your favourite programming language (Python, R, Julia?) and also explore the bibliographies of three mathematicians associated with the development of mathematics around these shapes. This is the next book in the Beta Bit series for anyone interested in computational mathematics and data analysis.
Download here
Published:
A one-of-a-kind 52-page story about responsible machine learning. Beta and Bit use decision trees, random forests, and AutoML tools to build a risk model after a covid infection, and then use explainable artificial intelligence tools to analyze the behavior of that model. The description of the data analysis process is intertwined with descriptions of ML tools and code snippets. All examples are fully reproducible!
Download here
Published:
How can you create good plots? Good ones, meaning ones that are viewed with pleasure, that are informative, that are understood by a wide audience but that still can be appreciated by connoisseurs.
Download here
Published:
A step-by-step introduction to the most important methods of explainable machine learning (XAI). Learn the intuitions, mathematical foundations and application examples of LIME, SHAP, Break Down, Partial Dependece, Permutational Variable Importance, Accumulated Local Effects and other popular techniques. Methods are enhanced with examples in R and Python using the DALEX library to explore any predictive model.
Download here
Generative AI is transforming how we work, but the extent of this change remains uncertain. Adopting these tools in academia presents unique challenges, yet this shift is inevitable. This blog series documents how I use selected GenAI tools in late 2024, offering insights that might inspire others in academia or serve as a personal reference for the early days of this transformation.
In this post, I delve into MidJourney, a versatile tool for generating creative visuals. I explain why I prefer it over similar tools, describe how I use it (e.g., for presentations, comics, and videos), and outline its strengths and ethical considerations. While MidJourney excels in visual storytelling, I caution against using it for research graphics or imitating contemporary artists’ styles. How do you use MidJourney? Share your thoughts in the comments!
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3). http://academicpages.github.io/files/paper3.pdf
Graduate course, University of Warsaw, MIM, 2023
Course materials are at https://github.com/mim-uw/eXplainableMachineLearning-2024
MSc seminars, University of Warsaw, MIM, 2023
Course materials are at https://github.com/mim-uw/MachineLearningSeminar