Quantitative Methods

  1. Introduction to Machine Learning
    1. Fundamental Concepts
      1. Types of Learning
        1. Supervised Learning
          1. Unsupervised Learning
            1. Semi-supervised Learning
              1. Reinforcement Learning
              2. Training and Testing
                1. Training Set
                  1. Validation Set
                    1. Test Set
                      1. Cross-Validation
                      2. Overfitting and Underfitting
                        1. Model Complexity
                          1. Bias-Variance Tradeoff
                            1. Regularization
                            2. Feature Engineering
                              1. Feature Selection
                                1. Feature Extraction
                                  1. Feature Scaling
                                    1. Handling Categorical Variables
                                  2. Supervised Learning
                                    1. Regression Methods
                                      1. Linear Regression
                                        1. Polynomial Regression
                                          1. Ridge Regression
                                            1. Lasso Regression
                                              1. Elastic Net
                                              2. Classification Methods
                                                1. Logistic Regression
                                                  1. k-Nearest Neighbors
                                                    1. Distance Metrics
                                                      1. Choosing k
                                                        1. Weighted k-NN
                                                        2. Decision Trees
                                                          1. Tree Construction
                                                            1. Splitting Criteria
                                                              1. Pruning Methods
                                                                1. Handling Missing Values
                                                                2. Naive Bayes
                                                                  1. Bayes' Theorem Application
                                                                    1. Gaussian Naive Bayes
                                                                      1. Multinomial Naive Bayes
                                                                      2. Support Vector Machines
                                                                        1. Linear SVM
                                                                          1. Non-linear SVM
                                                                            1. Kernel Functions
                                                                              1. Parameter Tuning
                                                                            2. Ensemble Methods
                                                                              1. Bagging
                                                                                1. Bootstrap Aggregating
                                                                                  1. Random Forest
                                                                                  2. Boosting
                                                                                    1. AdaBoost
                                                                                      1. Gradient Boosting
                                                                                      2. Stacking
                                                                                    2. Unsupervised Learning
                                                                                      1. Clustering
                                                                                        1. K-means Clustering
                                                                                          1. Hierarchical Clustering
                                                                                            1. DBSCAN
                                                                                              1. Gaussian Mixture Models
                                                                                              2. Dimensionality Reduction
                                                                                                1. Principal Component Analysis
                                                                                                  1. Independent Component Analysis
                                                                                                    1. t-SNE
                                                                                                      1. UMAP
                                                                                                      2. Association Rules
                                                                                                        1. Market Basket Analysis
                                                                                                          1. Apriori Algorithm
                                                                                                            1. Support and Confidence
                                                                                                          2. Model Evaluation
                                                                                                            1. Performance Metrics
                                                                                                              1. Regression Metrics
                                                                                                                1. Mean Absolute Error
                                                                                                                  1. Mean Squared Error
                                                                                                                    1. R-squared
                                                                                                                      1. Adjusted R-squared
                                                                                                                      2. Classification Metrics
                                                                                                                        1. Accuracy
                                                                                                                          1. Precision
                                                                                                                            1. Recall
                                                                                                                              1. F1-Score
                                                                                                                                1. Specificity
                                                                                                                                  1. ROC Curve
                                                                                                                                    1. AUC
                                                                                                                                      1. Confusion Matrix
                                                                                                                                    2. Model Selection
                                                                                                                                      1. Cross-Validation
                                                                                                                                        1. Information Criteria
                                                                                                                                          1. AIC
                                                                                                                                            1. BIC
                                                                                                                                            2. Grid Search
                                                                                                                                              1. Random Search
                                                                                                                                              2. Model Interpretation
                                                                                                                                                1. Feature Importance
                                                                                                                                                  1. Partial Dependence Plots
                                                                                                                                                    1. SHAP Values
                                                                                                                                                      1. LIME

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                                                                                                                                                  5. Computational Methods and Simulation

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                                                                                                                                                  1. Foundations of Quantitative Analysis