Introduction to Machine Learning– Objectives –importance in civil engineering - Linear Algebra - Probability theory –Probability Distribution- Statistical Decision theory -Bias Variance – convex optimization

Regression – Linear regression – Gaussian Process regression – Soil Contaminant Characterization – Neural network – Parameter Estimation & Back propagation – Regularization

Clustering and Dimension Reduction- K means – Principal component Analysis – Bayesian Networks – State Space models – Model Calibration

Introduction – Decisions –Utility theory – Utility Function – Value of Information – Sequential Decisions – Decision process – Model free decision process

MATLAB - Machine learning support in MATLAB - Tensor flow - Python Machine Learning – WEKA tools – Case study in civil engineering using machine learning theory