The objective of the course is to give a broad overview of the challenges and opportunities that exist in designing high performance AI systems. The course is designed to cater to two types of audiences:

• Students working on data science projects who want to understand how to perform faster training or inference of their AI/ML models. 
• Students working on parallel algorithms, or hardware acceleration, who want to understand modern techniques for accelerating data science applications. 

On the theory side, the course will cover basics and some recent advances in improving the performance of state-of-the-art AI/ML techniques including Convolutional Neural Networks (CNN), and Transformer based Large Language Models (LLM). 

On the practical side, the course will enable students to implement the optimizations to better grasp the concepts. Additionally, the course will discuss state-of-the-art programming languages and frameworks for accelerating AI such as Sycl/DPC++ for targeting CPU+Accelerator architectures and pytorch lightning for distributed AI/ML model training. 

The focus will be primarily on algorithmic optimizations as opposed to device specific optimizations.