Scientific machine learning (ML) seeks to address domain-specific data challenges and extract insights from scientific data sets through innovative methodological solutions, and this course aims to introduce scientific ML to senior students with a special focus on civil engineering applications. The course starts with an extensive review of statistics, the difference between ML and descriptive statistics, discusses sampling approaches for uncertainty quantification, then covers the fundamental knowledge of supervised learning (Bayesian linear regression, Gaussian processes, deep neural networks), unsupervised learning (k-means clustering, principal component analysis, Gaussian mixtures), and state space models (Kalman, particle filters). The course will further emphasize on the proper use of ML for civil engineering applications, including incorporating physics-based knowledge (physics-informed ML), dealing with data acquisition challenges (design of experiment, global optimization), and so on. Students will learn to address some unique challenges of applying ML to real-world engineering applications, preparing themselves better in their future career.