Dimensionality Reduction with PCA
Talk description (Track One)
Data is often high-dimensional - millions of pixels, frequencies, categories. A lot of this detail is unnecessary for data analysis - but how much exactly? This talk will discuss the basic principles and techniques of dimensionality reduction, provide (just a little!) mathematical intuition about how it's done, and use scikit-learn to show you how Netflix uses it to lead you from binge to binge.
About the speaker
Aabir is a researcher, currently at the Tata Institute of Fundamental Research in India. With an interdisciplinary background in engineering, physics, data science and social science, he enjoys tackling big problems at different scales, and is particularly fascinated by the theory of information. While not coding experiments and simulations, he enjoys playing the guitar, discussing philosophy and politics, and traveling.