Course Content

•  Overview of Semiconductor Physics and Devices
•  CMOS Process Flow
•  Crystal growth and doping
•  Unit processes: Lithography,  doping, etching …
•  Thin film growth

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