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ABOUT THE COURSE:
Increasing complexity of machine learning (ML) algorithms have necessitated the emergence of specialized computer systems. Some ML algorithms are executed at edge, some are executed on the cloud. In this course, we will delve into different computing kernels of both training and inference of ML algorithms and see how they can be efficiently computed. Specifically, we will cover different system optimization techniques for convolutional neural network (CNN), large language models (LLMs) and Graph Neural Networks (GNNs).
INTENDED AUDIENCE: Final year UG, Final year PG, PhD
PREREQUISITES: Computer Organization
INDUSTRY SUPPORT: NVIDIA, Intel, AMD, Microsoft, Google (whoever works in the junction of computer systems and machine learning)
Increasing complexity of machine learning (ML) algorithms have necessitated the emergence of specialized computer systems. Some ML algorithms are executed at edge, some are executed on the cloud. In this course, we will delve into different computing kernels of both training and inference of ML algorithms and see how they can be efficiently computed. Specifically, we will cover different system optimization techniques for convolutional neural network (CNN), large language models (LLMs) and Graph Neural Networks (GNNs).
INTENDED AUDIENCE: Final year UG, Final year PG, PhD
PREREQUISITES: Computer Organization
INDUSTRY SUPPORT: NVIDIA, Intel, AMD, Microsoft, Google (whoever works in the junction of computer systems and machine learning)
