This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion: What is learning? Can a machine learn? How to do it? How to do it well? Take-home lessons. The topics in the story line are covered by 18 lectures of about 60 minutes each plus Q&A. Lecture 1: The Learning Problem Lecture 2: Is Learning Feasible? Lecture 3: The Linear Model I Lecture 4: Error and Noise Lecture 5: Training versus Testing Lecture 6: Theory of Generalization Lecture 7: The VC Dimension Lecture 8: Bias-Variance Tradeoff Lecture 9: The Linear Model II Lecture 10: Neural Networks Lecture 11: Overfitting Lecture 12: Regularization Lecture 13: Validation Lecture 14: Support Vector Machines Lecture 15: Kernel Methods Lecture 16: Radial Basis Functions Lecture 17: Three Learning Principles Lecture 18: Epilogue