Optimization

Optimization Courses
Prerequisites: Foundation courses.
Recommended path
If you want to learn Optimization while referring back to ML and going through coding exercises, go for the CMU course.
If you want to learn the material mainly through a book, go with Boyd's textbook and course.
For a more approachable course or simply additional explanations, check out Ahmad Bazzi's videos.
Course
Year
Description
Difficulty Level
Resources
CMU - Convex Optimization​
2018
This course goes over fundamental topics in convex optimization while tying it back to Machine Learning and Statistics and it's a lot more computational than EE364A.
Medium
​🤖📕 🗒️​
Stanford - EE364A Convex Optimization​
2008
This course is essentially a walkthrough by Boyd of the topics covered in his book. It's a bit more difficult and fast-paced than the CMU course.
Medium
​📕 🗒️ ✍️​
Ahmad Bazzi - Convex Optimization​
2019
This course is very complementary to either of the above courses, but is a bit more approachable as it goes through the same/similar examples with more steps.
Medium
🗋
Supplementary Resources
Resource
Year
Description
Type
Stephen Boyd & Lieven Vandenberghe - Convex Optimization​
2004
The corresponding book for EE364A. The book ≈ a more detailed version of the lecture slides. 
📚

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