- Introduction to unsupervised learning (curse of dimensionality, PCA, ACI, NNMF)
- Introduction to supervised learning (overfitting, OLS, Ridge, LASSO, LDA, QDA)
- SVM (Large Margin Separator)
- Decision trees and random forests
- Ensemblistic learning
- Artificial neural networks (ANNs)
- Convolutional neural networks (CNNs)
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Learn the principles and assumptions underlying the different learning methods
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Understand the advantages and disadvantages of each method
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Learn how to use them on real, large-scale data