[Statistical Learning Modeling] · Statistical Classification Model (Logistic Regression) · Logistic Loss (Cross-Entropy Loss Function) · Gradient Descent (Stochastic Gradient Descent, Proximal Gradient Method) · Regularized Least Squares (Lasso, Ridge, Elastic Net) · Dimensionality Reduction (Feature Selection, Principal Component Analysis) [Image Processing, Cluster Analysis] · Median Filter, Gaussian Blur · Image Segmentation · Clustering Model (k-Means, k-Medoids, Fuzzy C-Means) · Dimensionality Reduction (Feature Selection, Principal Component Analysis, Singular Value Decomposition) [Feature Engineering, Statistical Learning Modeling] · Feature Engineering · Statistical Classification Model (k-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest) · Regularized Least Squares (Lasso) · Goodness of Fit and Model Selection (Cross-Validation, Overfitting) [Feature Engineering, Recommendation System] · Feature Scaling (Rescaling, Standardization, Robust Scaling) · Evaluation Metrics (Cosine Similarity, RMSE) · Recommendation System (Content-Based Filtering, Brute-Force-Based k-NN, KD-Tree-Based k-NN, Ball-Tree-Based k-NN)
