rishabhk108/OptimizationDemos

Some simple demos I use in my optimization in ML course. Includes implementations of ML loss functions (Logistic Loss, SVM Loss, ..) and optimization algorithms (gradient descent, accelerated variants, conjugate GD, etc.)

A repository containing all the demos for my "Optimization in Machine Learning Courses"

Here are the repositories for my Optimization in Machine Learning courses:

• Spring 2020: https://github.com/rishabhk108/OptimizationML
• Spring 2021: https://github.com/rishabhk108/AdvancedOptML

Prerequisites

• Numpy
• Scipy

Setup instructions

Other than the prerequisites listed above, this repository should be self constained. If you would like to try this out, feel free to clone this repo, open jupyter notebook and run this locally. Please reach out to rishabh.iyer@utdallas.edu if you have any difficulties running this.

Acknowledgments

I would like to acnowledge Mark Schmidt (https://www.cs.ubc.ca/~schmidtm/) from UBC for this. I converted his Matlab based tutorial to python. In particular, I used his summer school and tutorial slides as a reference!