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Machine Learning for Embedded Systems or CENG440

Output:
This course focuses on resource-constrained devices and introduces techniques and strategies for developing energy/time-efficient Machine Learning algorithms and models for embedded systems. Topics that are covered include Commonly used machine learning methods for embedded systems, model compression techniques on hardware platforms, hardware-aware machine learning, implementation and deployment of machine learning algorithms and models on embedded devices. This course also fosters students’ hands-on skills development experience through team-based projects and presentations.