MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023 - Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions

MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023 Instructors: Alan Edelman, Steven G. Johnson View the complete course: https://ocw.mit.edu/courses/18-s096-matrix-calculus-for-machine-learning-and-beyond-january-iap-2023/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62EaLLH92E_VCN4izBKK6OE Description: Many systems are modeled by ordinary differential equations (ODEs), and often you want the derivative of the ODE solution with respect to parameters of the equations. An efficient way to do this is often to solve a second “adjoint” ODE. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.