Caltech free online course: Learning from Data
Free, introductory Machine Learning online course, taught by a top-rated Caltech professor. Lectures recorded from a live broadcast, including QnA.
Caltech Machine Learning course
A real Caltech course, not a watered-down version
- Free, introductory Machine Learning online course (MOOC)
- Taught by Caltech Professor Yaser Abu-Mostafa [article]
- Lectures recorded from a live broadcast, including Q&A
- Prerequisites: Basic probability, matrices, and calculus
- Homeworks with online grading and ranking
- Discussion forum for participants
- Statement of Completion issued free of charge
You can also offer the course at your university via self-service or full-service.
The 18 lectures are 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
Register and learn more at work.caltech.edu/telecourse