This course covers core concepts in machine learning (models and algorithms) from a probabilistic perspective. It is structured into five modules: foundations, linear methods, deep neural networks, nonparametric methods and unsupervised learning. Applications to various subdisciplines of engineering will be highlighted, especially in transportation, environmental, structural and industrial engineering. Hands-on programming in Python/R throughout the course will enable students to implement models on real-world datasets. Through this course, students will gain a thorough knowledge of the probabilistic modeling approach to machine learning and maste state-of-the-art practical aspects in order to solve challenging problems.


The main text for the course is Kevin’s Murphy’s Probabilistic Machine Learning: An Introduction