Applied Machine Learning

Catalog description

This course covers a wide variety of machine learning topics balancing between theory of machine learning and practical applied skills. This course addresses how to solve machine learning problems (supervised and unsupervised) using techniques from both traditional machine learning and deep learning by leveraging standard, modern Python tooling such as scikit-learn and tensorflow. The course will cover additional topics such as bias and fairness in machine learning, data pipeline basics, and model deployment basics, Students will also complete a semester long project demonstrating an end-to-end machine learning application. The course involves writing Python code both for labs, homework, and exams.