Convex objective functions are the ones we understand the best. This video explains how things like linear programming can capture real-world optimization problems. 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.
Optimization is a huge topic of tremendous importance. In this video we hit some of the high points to give you the big picture. 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.
Information theory is a fascinating topic that informs many of the ways we think about structure in data. This video provides a brief overview of some of the concepts, inspired by the lectures of my PhD advisor, the late David MacKay. 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.
It’s difficult to overstate the centrality of the Gaussian distribution to machine learning, statistics, and many other natural sciences. This video talks about some of the reasons this distribution is special. 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.
This video talks about Chebyshev, Markov, Jensen, and friends. 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.
To do interesting things with joint distributions, they need to have dependence structure. 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.
From a modeling point of view, joint probability distributions are where the action is. They let you posit latent structure and reason about conditional probability. This video gives an overview of the basics of joint random variables. 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.
There are some distributions that come up over and over again in machine learning and statistics. In this video, I give an overview of some distributions to be aware of. 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.
Probability mass functions and probability density functions are key objects for reasoning about uncertainty. This video by Ari Seff gives an introduction. 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.
Probability is a big part of many aspects of machine learning, and this video looks at some of the basic formalisms for it. 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.