Probability is a big part of many aspects of machine learning, but this may not be totally obvious from the outset. In this video we take a look at different ways that probability informs ML. This is part of a series of videos for COS 302: Mathematics for Numerical Computation and Machine Learning, replacing lectures after the course went remote due to the COVID-19 pandemic.
We often talk about the gradient of a scalar function as being the direction of steepest ascent. Rather than taking that for granted, let’s convince ourselves that it is true. This is part of a series of videos for COS 302: Mathematics for Numerical Computation and Machine Learning, replacing lectures after the course went remote due to the COVID-19 pandemic.
A useful way to think about derivatives (and gradients/Jacobians more generally) is as the maps that give you the best affine approximation at a point. This is part of a series of videos for COS 302: Mathematics for Numerical Computation and Machine Learning, replacing lectures after the course went remote due to the COVID-19 pandemic.
We think about gradients a lot in machine learning. This video talks about partial derivatives in general and Jacobian matrices, which specialize to gradients for scalar functions. This is part of a series of videos for COS 302: Mathematics for Numerical Computation and Machine Learning, replacing lectures after the course went remote due to the COVID-19 pandemic.
Differentiation is fundamental to lots of different aspects of machine learning. This video starts at the beginning and talks about the limit of the difference quotient, the product rule, and the chain rule. This is part of a series of videos for COS 302: Mathematics for Numerical Computation and Machine Learning, replacing lectures after the course went remote due to the COVID-19 pandemic.
A few years ago, I co-founded the Talking Machines podcast with Katy Gorman and we co-hosted it together for two seasons. After those two seasons, I decided it was time to move on and let someone else host it, but it was a highly rewarding experience. Since then, I’ve contemplated starting a YouTube channel, motivated in part by that outreach experience and also by the amazing people on the platform who I’ve enjoyed watching and learning from. I’ve often felt there was a hole on machine learning topics on YouTube. There are zillions of deep learning “explainer” videos and of course many online lectures, but there’s not a lot that 1) explains the broader technical context of machine learning, and … Read More