Description
Machine learning basics:
- Bias-Variance Tradeoff
+ Cross-Validation
+ Training, Validation and Test sets
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The image classification problem:
- Linear approach
- k-neareast neighbour
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Data driven approach:
- Loss function
- Regularization
- Optimization
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Back propagation
