Computer Vision

  1. Classical Machine Learning for Vision
    1. Pattern Recognition Foundations
      1. Feature Engineering
        1. Feature Extraction Techniques
          1. Feature Selection Methods
            1. Dimensionality Considerations
            2. Learning Paradigms
              1. Supervised Learning
                1. Unsupervised Learning
                  1. Semi-Supervised Learning
                  2. Training and Validation
                    1. Training Set Construction
                      1. Validation Strategies
                        1. Test Set Evaluation
                      2. Classification Algorithms
                        1. Nearest Neighbor Methods
                          1. k-Nearest Neighbors
                            1. Distance Metrics
                              1. Curse of Dimensionality
                              2. Support Vector Machines
                                1. Linear SVM
                                  1. Non-Linear SVM
                                    1. Kernel Functions
                                      1. Polynomial Kernels
                                        1. RBF Kernels
                                          1. Custom Kernels
                                          2. Multi-Class Extensions
                                          3. Decision Trees
                                            1. Tree Construction
                                              1. Splitting Criteria
                                                1. Pruning Strategies
                                                2. Ensemble Methods
                                                  1. Random Forests
                                                    1. Boosting Algorithms
                                                      1. Bagging Methods
                                                    2. Dimensionality Reduction
                                                      1. Principal Component Analysis
                                                        1. Eigenvalue Decomposition
                                                          1. Variance Preservation
                                                            1. Applications in Vision
                                                              1. Eigenfaces
                                                                1. Dimensionality Reduction
                                                              2. Linear Discriminant Analysis
                                                                1. Fisher's Linear Discriminant
                                                                  1. Multi-Class LDA
                                                                    1. Comparison with PCA
                                                                    2. Non-Linear Methods
                                                                      1. Kernel PCA
                                                                        1. Manifold Learning
                                                                          1. t-SNE Visualization
                                                                        2. Clustering Algorithms
                                                                          1. Partitional Clustering
                                                                            1. K-Means Algorithm
                                                                              1. K-Medoids Algorithm
                                                                                1. Initialization Strategies
                                                                                2. Hierarchical Clustering
                                                                                  1. Agglomerative Clustering
                                                                                    1. Divisive Clustering
                                                                                      1. Linkage Criteria
                                                                                      2. Density-Based Clustering
                                                                                        1. Mean-Shift Algorithm
                                                                                          1. DBSCAN Algorithm
                                                                                          2. Model-Based Clustering
                                                                                            1. Gaussian Mixture Models
                                                                                              1. Expectation-Maximization
                                                                                            2. Performance Evaluation
                                                                                              1. Cross-Validation Techniques
                                                                                                1. k-Fold Cross-Validation
                                                                                                  1. Leave-One-Out Cross-Validation
                                                                                                    1. Stratified Cross-Validation
                                                                                                    2. Classification Metrics
                                                                                                      1. Confusion Matrix
                                                                                                        1. Accuracy Measures
                                                                                                          1. Precision and Recall
                                                                                                            1. F1-Score
                                                                                                              1. ROC Curves and AUC
                                                                                                              2. Clustering Evaluation
                                                                                                                1. Internal Validation
                                                                                                                  1. External Validation
                                                                                                                    1. Silhouette Analysis

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                                                                                                                7. Motion Analysis and Video Processing

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                                                                                                                9. Deep Learning for Computer Vision