Intro to Machine Learning, Hypothesis Spaces, Inductive Bias. Supervised Learning: Linear and Logistic Regression. Cross Validation (CV). Support Vector Machines (SVMs) and Regression. Empirical Risk Minimization and Regularization. Unsupervised Learning: Clustering and PCA. Decision Trees, Ensembles and Random Forest. Neural Net Fundamentals. Engineering Design considerations for Deployment: Explainability, Interpretability, Bias and Fairness, Accountability, Ethics, Feedback Loops, and Technical Debt.