Bias in Machine Learning with Rachel Thomas

Available on: iTunes | Android | RSS

Most of us have come across a form of bias when we interact with others. These biases can make their way to a machine learning system, leading to unfair decisions. Rachel Thomas, co-founder of fast.ai and researcher in residence at The University of San Francisco explains the origins and implications of bias in machine learning. We also talked about solutions to limit bias.

Rachel also explained the role of linear algebra in machine learning and how to teach it effectively for people working in ML applications. We talked about the fundamental concepts and how they are applied in machine learning. Check out the Computational Linear Algebra course on fast.ai.