Lecture 1 | Convex Optimization I (Stanford)
Why Hacker News recommends it
HN recommends Boyd's lectures as a fantastic, often funny follow-up to linear algebra and as a step in machine learning study paths.
What it is
The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.
Recommended by 2 HN commenters across 2 different months, 2011 to 2022.
What Hacker News said
Strong rec natly · 2022-05 · on “The big six matrix factorizations”
These lectures are fantastic after you've mastered the basics of linear algebra [this video] (convex optimization, by a very experienced and often funny lecturer)
Recommends hwers · 2022-01 · on “Matrix Multiplication”
This is my favorite way to think about it as well. I think I learned it first in one of Steven Boyds astounding convex optimization lectures [this video]
Cites wcsun · 2011-06 · on “Ask HN: How do I start with Machine Learning?”
First, finish lectures by Professor Gilbert Strang. [LINK] To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. [LINK] [LINK] If you just want to use the libraries, you can stop here. If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. [LINK] Then, you can try to finish EE364 and watch the videos. [LINK] [this video] If you want to roll your own algorithms, you have to know some optimization tools. [LINK] And there is some statistics knowledge you have to fill in. I used these: [LINK] [LINK] R is used in the courses.
Cites zxcvvcxz · 2015-04 · on “The Importance of Mathematics Courses in Computer Science Education”
A practical math topic I think all computer science (and engineering, really) students should take is convex optimization. Often a problem in engineering or computer science can be phrased as minimizing a cost function over some data and/or variable constraints. Take SVM for example, or sparse signal denoising, or template matching. And far too often, people will apply "advanced state of the art techniques" to solve these problems, e.g. neural networks of some exotic kind, graphical models, etc. And while these are great techniques, they need not be applied to convex problems of a reasonable size. In the examples I listed, I can almost guarantee that a knowledge of how to phrase the model as