Introduction to machine learning (ML): core elements and terminologies, underlying mathematical philosophy, solution categories, general work-flow of ML solutions development, and examples of ML-based solutions in various domains. Cover the mathematical bases and algorithms of various supervised and unsupervised learning methods. Discuss and develop ML strategies for selecting/assessing the right ML approach for a given environmental science or environmental engineering problem.

Theory and practice of implementing machine learning (ML) techniques for problems in environmental science and engineering. Discuss the inner workings of various ML algorithms: k-nearest neighbors (k-NN), naive Bayes, decision trees, artificial neural network (ANN), support vector machines (SVM), k-means clustering, and more. Emphasis on practical application and techniques with real environmental problems and data.