Global Certificate in Statistical Pattern Recognition Systems
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โข Statistical Theory: Probability distributions, Bayesian decision theory, hypothesis testing
โข Data Preprocessing: Data cleaning, normalization, feature extraction
โข Feature Selection and Dimensionality Reduction: Filter, wrapper, and embedded methods, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
โข Classification: Parametric and non-parametric methods, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Decision Trees, Random Forests
โข Neural Networks: Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
โข Unsupervised Learning: Clustering algorithms, hierarchical clustering, K-means
โข Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curve, AUC
โข Special Topics: Ensemble methods, deep learning, transfer learning, one-shot learning
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