Learning from Data (Introductory Machine Learning course)

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 adaptively improve their performance with experience accumulated from the observed 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 below 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.

• 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