This course covers complexity results for first order and related optimization algorithms. Such algorithms are widely applied to numerous problems in machine learning, signal processing, communications, etc. and have witnessed a surge in popularity since the advent of Big Data around 2005. Wherever possible, we will attempt to follow a generic template for the proofs, thereby unifying a large number of results in the literature.

https://vipular.github.io/stuff/2021_MLSP.html 

• This course will discuss statistical signal processing from both theoretical and practical perspectives.
• It will include the basics of Estimation and Detection Theory, Parametric, and Non-parametric spectrum estimation (including ML, MAP, LSE). The course will have a balanced focus on math formulations and practice. It will involve a Project/Term paper implementation and presentation to allow for more space to assimilate the concepts as well.
• Discussion Hour: 
• Zoom Link for Discussion Hour given in the Announcement section