Interpretable Machine Learning News Today

Students can choose six electives from the list of available courses offered by the Centre of Excellence in Artificial Intelligence and some other departments of IIT Kharagpur.

Researchers are using Machine Learning to speed up the discovery of proton-conducting oxides, which are important in hydrogen fuel cells.

Misinformation watchdogs are concerned that it’s becoming harder than ever to determine what is real and fake online.

Saliency cards, developed at MIT and IBM, seek to describe a machine-learning model’s behavior and to help users quickly compare methods so they can choose the right one for their task.

MyJournals.org - Science - [ASAP] Advancing Computational Toxicology by Interpretable Machine Learning (Environmental Science & Technology)

Scientists discuss a machine learning-based tool that can detect Parkinson's disease years before the first onset of symptoms.

Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.The first section of the book is a beginners guide to interpretability, covering its relevance in business and exploring it...

Some day machine learning models may be more ‘glass box‘ than black box. Until then, ‘XAI’ tools and techniques can help us understand how a black box model makes its decisions.

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false negative predictions. That’s sometimes acceptable, but matters a lot when the stakes are high. For example, a drone weapons system that falsely identifies a school as […]

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.