RSEG 113

Software Engineering with Machine Learning

Machine learning systems learn on data and then continue to evolve and enhance their models autonomously based on the feedback they receive. Today’s machine learning powers autonomous systems such as self-driving cars, robotic systems, financial trading models, fraud prevention, drug discovery algorithms, targeted marketing and virtual assistants, among others. This course will focus on Machine Learning topics and their applications in real-life systems, including: machine learning system architecture; machine learning algorithm types (supervised, unsupervised, semi-supervised, reinforcement, and deep learning); machine learning tools, software and frameworks; classification algorithms; neural networks; approaches to creating machine learning applications; and ethical aspects of machine learning. Students will practice creating machine learning models and will review the use of machine learning models in domains such as healthcare, business, education, engineering, smart cities and transportation.

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