White-box AutoML: Techniques for Creating Interpretable Models
Register to Attend | March 3 | 12 PM SGT (3 PM AEDT)
An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI
At H2O, our mission is to democratize AI, and Responsible AI and Machine Learning Interpretability are the critical topics for ML adoption in commercial applications and in our day-to-day lives.
As fields like explainable AI have continued to develop, we have seen a litany of new methodologies that can be applied to improve our ability to trust and understand our machine learning and deep learning models.
In this webinar we examine a set of machine learning techniques, algorithms, and models to help data scientists improve the accuracy of their predictive models while maintaining a high degree of interpretability. We will discuss the most recent and disruptive breakthroughs in explainability, fairness, and interpretability techniques for machine learning and share how machine learning interpretability is applied in practice.
Join this session with Commonwealth Bank of Australia & H2O.ai on March 3rd at 3 PM AEDT | 12 PM SGT to learn:
- Common & Breakthrough Interpretability Techniques
- Limitations and Precautions
- Testing Interpretability and Fairness
- Machine Learning Interpretability in Action
Executive Manager AI Labs, Commonwealth Bank of Australia
Senior Data Scientist, H2O.ai
3x Kaggle Grandmaster