This course provides an overview of machine learning systems, leading to practical implementation and application for embedded hardware devices. Topics include supervised and unsupervised learning systems, performance evaluation metrics, feature engineering, dimensionality reduction, data fusion and hardware implementation. The students will implement, test and analyze a machine learning systems using both software and hardware techniques. Weekly hours: 3 Lecture hoursPrerequisite(s): EE 216.3
This course provides an overview of machine learning systems, leading to practical implementation and application for embedded hardware devices. Topics include supervised and unsupervised learning systems, performance evaluation metrics, feature engineering, dimensionality reduction, data fusion and hardware implementation. The students will implement, test and analyze a machine learning systems using both software and hardware techniques. Weekly hours: 3 Lecture hoursPrerequisite(s): EE 216.3
