14 Machine learning foundations for vision
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14.1 The learning paradigm
14.1.1 Supervised, unsupervised and self-supervised learning
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14.1.2 Training, validation and test sets
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14.1.3 Bias, variance and generalisation
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14.1.4 Cross-validation and model selection
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14.2 Classical classifiers
14.2.1 Linear and logistic regression
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14.2.2 K-nearest neighbours
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14.2.3 Support vector machines and kernels
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14.2.4 Decision trees, random forests and boosting
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14.2.5 Bayesian classifiers
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14.3 Feature engineering and dimensionality reduction
14.3.1 Hand-crafted image features
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14.3.2 Principal component analysis
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14.3.3 Linear discriminant analysis
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14.3.4 Manifold learning (outline)
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14.4 Loss functions and optimisation
14.4.1 Squared, absolute and Huber loss
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14.4.2 Cross-entropy and log-loss
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14.4.3 Gradient descent and its variants
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14.4.4 Regularisation: L1, L2, elastic net
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14.5 Evaluation of classifiers
14.5.1 The confusion matrix
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14.5.2 Accuracy, precision, recall, F-score
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14.5.3 ROC and precision-recall curves; AUC
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14.5.4 Class imbalance and industrial reality
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14.6 Choosing classical versus deep approaches
14.6.1 Data availability and labelling cost
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14.6.2 Interpretability and validation burden
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14.6.3 When hand-crafted features still win
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