09-15, 10:00–11:20 (Europe/Amsterdam), Hello, World! (Tutorials)
Knowledge work is undergoing a transformative journey with machine learning (ML) but the interpretability of the models we interact with is still lagging behind the coolness and hype of the technologies using ML. This workshop seeks to address the gap between the speed at which we use and adopt ML and the pace at which we understand it. During the workshop, we will cover fundamental concepts and techniques of interpretable machine learning, and explore various explainability methods supported by the Alibi Explain library so that you can get started explaining your models. If you've been meaning to dive deeper into the field of interpretable ML, add interpretability to your workflows, find an alibi for your models, or are simply curious about the field, come and join us for a fun 90-minute interactive session on interpretable ML.
In the era of complex and powerful machine learning (ML) models, understanding the decision-making process of these models has become a challenge, and different interpretability methods can help us build trust, address bias, and ensure compliance with different standards. The Alibi Explain library offers a comprehensive toolkit for interpreting machine learning models and shedding light on their inner workings. No prior experience with the library is required, but some knowledge of machine learning is expected.
During the workshop, we will cover the fundamental concepts and techniques of interpretable machine learning, exploring various explainability methods supported by Alibi Explain. We will discuss strategies such as rule-based explanations, feature importance, and counterfactual explanations. Through hands-on exercises, participants will gain practical experience in interpreting models and understanding their predictions.
Throughout the workshop, we will emphasize real-world applications and use cases to demonstrate the relevance and importance of interpretable machine learning. We will discuss how interpretability can enable better decision-making in finance, healthcare, and retail domains. By the end of the workshop, attendees will have a good understanding of interpretable machine learning concepts and practical skills for finding an alibi for their ML models.
No previous knowledge expected
Hello! I'm Ramon, a data scientist, researcher, and educator living in Sydney. I currently work as a freelance data professional and was previously a Senior Product Developer at Decoded, a technology education company based in the UK. While at Decoded, I created custom data science tools, workshops, and training programs for clients in industries ranging from retail to finance. Prior to that, I held roles at the intersection of education, data science, and research in the areas of entrepreneurship and strategy, alongside a few ventures in consumer behavior and development economics research in industry and academia, respectively. On the personal side, I enjoy giving talks and technical workshops and have had the privilege of participating in several conferences such PyCon, SciPy, PyData, and countless meetup events. In my spare time, I spend as much time as possible mountain biking and exploring many of the outdoor wonders Australia has to offer.