Introduction to Applied Machine Learning. 3 credits, 4 contact hours

Catalog description

Restrictions: The course is open for students with junior (and above) standing. The course is composed of two main parts: 1) basic applied machine learning techniques including deep learning, regression, classification, convolutional neural networks, generative adversarial networks, and model compression; and 2) introduction to PyTorch, colab, and jupyter notebook and provide students with hands-on experience of developing and implementing machine learning solutions, on mobile and IoT devices for cyber-physical systems.