*ML 7 - [Time Series Anomaly Detection, Feature Engineering, Statistical/Deep Learning Modeling] — Ki B. Kim

[Time Series Anomaly Detection, Feature Engineering, Statistical Learning Modeling, Deep Learning Modeling]

· Data Transformation (Logarithm, Box-Cox)

· Feature Scaling (Rescaling, Standardization, Robust Scaling, L2 Normalization)

· Dimensionality Reduction (PCA, LDA, t-SNE, UMAP)

· Statistical Learning Modeling (Isolation Forest, One-Class SVM)

· Deep Learning Modeling (LSTM, GRU, CNN, Autoencoder, VAE)

· Deep Learning Optimization (Layer Details, Batch Size, Learning Rate, Node, Optimizer Function, Pruning, Stateful, Callback, Activation Function)

· Classification Evaluation Metrics (MSE and MAE for Loss Function, Accuracy, Precision and TPR Recall, TPR Sensitivity and TNR Specificity, F-Score, ROC Curve, RMSE, MAPE, Kappa)

[Anomaly Detection, Vibration Analysis, Statistical Learning Modeling, Deep Learning Modeling]

· Vibration Analysis (Spectrogram, Fast Fourier Transform, Wavelet Transform, Power Spectral Density)

· Vibration Data Labeling (Statistical Dynamic Threshold)

· Statistical Learning Modeling (Isolation Forest, One-Class SVM)

· Deep Learning Modeling (LSTM, GRU, CNN, Autoencoder, VAE)

· Deep Learning Optimization (Layer Details, Batch Size, Learning Rate, Node, Optimizer Function, Pruning, Stateful, Callback, Activation Function)

· Classification Evaluation Metrics (MSE and MAE for Loss Function, Accuracy, Precision and TPR Recall, TPR Sensitivity and TNR Specificity, F-Score, ROC Curve, RMSE, MAPE, Kappa)

*Machine Learning 6 - [Evolutionary Algorithm, Deep Learning Modeling, Bayesian Network]

**Big Data 1 - [Big Data Analytics, ETL Pipeline, Cloud Computing]