Abstract: Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs boson, machine learning excells at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to ""learn"" a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formalizing a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. The talk should give you all the necessary background to start using machine learning yourself.
Bio: Thomas Fan is a Software Developer at Columbia University's Data Science Institute. He collaborates with the scikit-learn community to develop features, review code, and resolve issues. On his free time, Thomas contributes to skorch, a scikit-learn compatible neural network library that wraps PyTorch.
Software Developer - Machine Learning | Columbia Data Science Institute