natlyon Hacker News • Recommends
On “The big six matrix factorizations”
These lectures are fantastic after you've mastered the basics of linear algebra link to this video (convex optimization, by a very experienced and often funny lecturer)
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#convexoptimization#linearalgebra#leastsquares
Lecture 1 | Convex Optimization I (Stanford)
Stanford
2.2M subscribers • 18 videos on Hallway
- Recommended by 2 people: 2 on Hacker News.
- Presented by Stephen Boyd, Professor at Stanford University
- May be out of date. Uploaded 18 years ago, in an area where tools change fast.
Advanced. Deep dive for people who know the field.
753K views18 years ago2 recommendations
Computer scienceAI and machine learningMath for programmersAlgorithmsMath for machine learning
Professor Stephen Boyd delivers the introductory lecture on convex optimization, covering convex sets, functions, and engineering applications.
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, gives the introductory lecture for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. Complete Playlist for the Course: youtube.com/view_play_list?p=3940DD956CDF0622 EE 364A Course Website: stanford.edu/class/ee364 Stanford University: stanford.edu/ Stanford University Channel on YouTube: youtube.com/stanford/Why it's here
2 people recommend thisWhat people say. Highly recommended as an outstanding and engaging lecture series on convex optimization for computer science and engineering students.
hwerson Hacker News • Recommends
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 link to this video
wcsunon Hacker News • Mentions
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 link to 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.
zxcvvcxzon Hacker News • Mentions
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…