[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)
