Why should I care?
The first quarter of 2021 has brought us common knowledge about the importance of genomic sequencing for battling COVID-19. Apparently, monitoring SARS-CoV-2 variants and mutations in countries will be crucial to epidemy spread, vaccine development, and efficiency. There is a well-written sequence of interesting news on this topic in Nature:
Are explainability methods black-box themselves?
There are various adversarial attacks on machine learning models; hence, ways of defending, e.g. by using Explainable AI methods. Nowadays, attacks on model explanations come to light, so does the defense to such adversary. Here, we introduce fundamental concepts related to the domain. A further reference list is available at https://github.com/hbaniecki/adversarial-explainable-ai.
When considering an explanation as a function of model and data, there is a possibility to change one of these variables to achieve a different result.
How to tell a good story?
Previous part: Data Visualization Cheat Sheets
Never would have thought that teaching the Data Visualization Techniques course for second-year Data Science students at the Warsaw University of Technology would bring me so much satisfaction. In the second project, we asked students to prepare posters concerning COVID-19 statistics, while also exploring various topics like restrictions, tourism, air pollution, economy, etc.
There were many impressive visualizations, and here are the best results (more can be found at GitHub).
How to make a good graph?
I have an opportunity to co-teach a Data Visualization Techniques course for second-year Data Science students at the Warsaw University of Technology.
As a part of the course, we asked students to prepare informative posters concerning good plotting practices and specific cheat sheets for data visualization.
The subject of the project was rather open, and here are the best results (more can be found at GitHub).
I will showcase how straightforward and convenient it is to explain a tensorflow predictive model using the dalex Python package. The introduction to this topic can be found in Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. For any inquiries, please contact me on LinkedIn or GitHub.
For this example, we will use the data from the World Happiness Report and predict the happiness scored according to economic production, social support, etc. for any given country.
Explainable machine learning, responsibility, robustness, and adversarial attacks. Developing open-source Python & R packages.