CS1156x: Caltech 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 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.

Course Prerequisites

1. Basic probability,
2. matrices, and
3. calculus.
4. Familiarity with some programming language or platform will help with the homework.

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 (SVM)

Lecture 15: Kernel Methods

Lecture 16: Radial Basis Functions

Lecture 17: Three Learning Principles

Lecture 18: Epilogue

References

1. Caltech Machine Learning Library
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