This course provides an overview of machine intelligence and the role of machine learning for making informed data-driven decisions and solving a variety of real-world problems in engineering, computer science or related fields. The course starts with a mathematical and statistical background required for machine learning and covers models, algorithms, and approaches for supervised learning, unsupervised learning, etc., as well as their applications. This course will also discuss recent advances in data analytics such as data modeling, evaluation, data communication and visualization, and data ethics that are critical to machine learning. Topics covered will be illustrated with MATLAB or other software packages for a range of applications, for example, information process, signal/image processing, pattern recognition, system identification, control, agriculture and farming, and healthcare, etc.
Prerequisites: ENGE 320 or MATH 309 or equivalent.
ENEE 422 Introduction to Machine Learning
