This is an introductory course in machine learning tailored for IME students. It covers topics from classification, regression and statistical signal processing, to more recent techniques such as neural networks and deep learning. It also covers the analog approximate computing integrated circuit design considerations for acceleration purposes. The course aims to offer students the fundamental concepts in advanced artificial intelligence theory with an emphasis on hands-on experience through practical examples such as intelligent hardware system implementation and case studies with MATLAB/Python. The verified algorithm can be further implemented on an FPGA for applications such as image/audio recognition.

CILO-1: Apply the essential knowledge in machine learning and deep learning. CILO-2: Design analog accelerators with practical circuit considerations. CILO-3: Design and verify neural networks for image/audio classification problems using MATLAB/Python.