#statistical machine learning

A beautiful, thoughtful article about how to learn, think for yourself, and love to learn. The author loves humans.

Statistical Machine Learning is a field that combines statistics and computer science to develop algorithms that learn patterns and make predictions from data. It focuses on building probabilistic models that can infer underlying structures and quantify uncertainty. Unlike purely algorithmic approaches, it emphasizes understanding data through statistical principles, enabling robust and interpretable solutions. This approach is widely used in areas such as natural language processing, computer vision, and bioinformatics. By integrating statistical theory with machine learning, it helps solve complex real-world problems with data-driven insights.

Dr. Deng is widely published for his work in acoustic phonetics, speech processing, translation, and machine learning. As a principle researcher at Microsoft Research, he uses deep neural networks for speech recognition and is among the leading innovators in his field. He is also a Fellow of The Acoustical Society of America, the IEEE, and the ISCA.

Hilary Mason, Chief Scientist at bit.ly, co-founder of HackNY, and NYC Resistor member gives a talk titled, "Small Urls, Big Fun: Understanding the World in Realtime" (2012)

Google Director of Research, Peter Norvig, talks about the awesomeness of data (2010)