CaltechX: Learning From Data (Introductory Machine Learning)

• Identify basic theoretical principles, algorithms, and applications of Machine Learning
• Elaborate on the connections between theory and practice in Machine Learning
• Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations

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