Introduction to Artificial Intelligence

  1. Machine Learning Fundamentals
    1. Core Concepts and Terminology
      1. Learning Problems
        1. Supervised Learning
          1. Unsupervised Learning
            1. Reinforcement Learning
              1. Semi-Supervised Learning
              2. Data and Datasets
                1. Training Data
                  1. Validation Data
                    1. Test Data
                      1. Data Splitting Strategies
                      2. Features and Target Variables
                        1. Feature Types
                          1. Feature Engineering
                            1. Feature Selection
                              1. Dimensionality
                              2. Model Complexity and Generalization
                                1. Overfitting
                                  1. Underfitting
                                    1. Bias-Variance Tradeoff
                                      1. Regularization Concepts
                                      2. Performance Evaluation
                                        1. Cross-Validation
                                          1. Holdout Method
                                            1. Bootstrap Sampling
                                          2. Supervised Learning
                                            1. Regression Analysis
                                              1. Linear Regression
                                                1. Simple Linear Regression
                                                  1. Multiple Linear Regression
                                                    1. Least Squares Method
                                                      1. Gradient Descent
                                                        1. Normal Equation
                                                        2. Polynomial Regression
                                                          1. Feature Transformation
                                                            1. Degree Selection
                                                            2. Regularized Regression
                                                              1. Ridge Regression (L2)
                                                                1. Lasso Regression (L1)
                                                                  1. Elastic Net
                                                                2. Classification Methods
                                                                  1. Logistic Regression
                                                                    1. Sigmoid Function
                                                                      1. Maximum Likelihood Estimation
                                                                        1. Multiclass Classification
                                                                        2. k-Nearest Neighbors
                                                                          1. Distance Metrics
                                                                            1. Choosing k
                                                                              1. Weighted k-NN
                                                                                1. Curse of Dimensionality
                                                                                2. Support Vector Machines
                                                                                  1. Linear SVM
                                                                                    1. Soft Margin
                                                                                      1. Kernel Methods
                                                                                        1. Nonlinear SVM
                                                                                          1. Multi-class SVM
                                                                                          2. Decision Trees
                                                                                            1. Tree Construction
                                                                                              1. Splitting Criteria
                                                                                                1. Pruning Techniques
                                                                                                  1. Handling Continuous Features
                                                                                                  2. Ensemble Methods
                                                                                                    1. Bagging
                                                                                                      1. Random Forests
                                                                                                        1. Boosting
                                                                                                          1. AdaBoost
                                                                                                            1. Gradient Boosting
                                                                                                          2. Model Evaluation Metrics
                                                                                                            1. Regression Metrics
                                                                                                              1. Mean Squared Error
                                                                                                                1. Mean Absolute Error
                                                                                                                  1. R-squared
                                                                                                                  2. Classification Metrics
                                                                                                                    1. Accuracy
                                                                                                                      1. Precision and Recall
                                                                                                                        1. F1-Score
                                                                                                                          1. Confusion Matrix
                                                                                                                            1. ROC Curves and AUC
                                                                                                                              1. Precision-Recall Curves
                                                                                                                          2. Unsupervised Learning
                                                                                                                            1. Clustering Algorithms
                                                                                                                              1. k-Means Clustering
                                                                                                                                1. Algorithm Steps
                                                                                                                                  1. Initialization Methods
                                                                                                                                    1. Choosing Number of Clusters
                                                                                                                                      1. Limitations
                                                                                                                                      2. Hierarchical Clustering
                                                                                                                                        1. Agglomerative Clustering
                                                                                                                                          1. Divisive Clustering
                                                                                                                                            1. Linkage Criteria
                                                                                                                                              1. Dendrograms
                                                                                                                                              2. Density-Based Clustering
                                                                                                                                                1. DBSCAN
                                                                                                                                                  1. Mean Shift
                                                                                                                                                  2. Gaussian Mixture Models
                                                                                                                                                    1. EM Algorithm
                                                                                                                                                      1. Model Selection
                                                                                                                                                    2. Dimensionality Reduction
                                                                                                                                                      1. Principal Component Analysis
                                                                                                                                                        1. Covariance Matrix
                                                                                                                                                          1. Eigenvalues and Eigenvectors
                                                                                                                                                            1. Variance Explained
                                                                                                                                                            2. Linear Discriminant Analysis
                                                                                                                                                              1. Supervised Dimensionality Reduction
                                                                                                                                                                1. Class Separability
                                                                                                                                                                2. Non-linear Methods
                                                                                                                                                                  1. t-SNE
                                                                                                                                                                    1. UMAP
                                                                                                                                                                      1. Manifold Learning
                                                                                                                                                                    2. Association Rule Learning
                                                                                                                                                                      1. Market Basket Analysis
                                                                                                                                                                        1. Apriori Algorithm
                                                                                                                                                                          1. Support and Confidence
                                                                                                                                                                            1. Lift and Conviction
                                                                                                                                                                          2. Reinforcement Learning
                                                                                                                                                                            1. Markov Decision Processes
                                                                                                                                                                              1. States and Actions
                                                                                                                                                                                1. Transition Probabilities
                                                                                                                                                                                  1. Reward Functions
                                                                                                                                                                                    1. Policies
                                                                                                                                                                                      1. Value Functions
                                                                                                                                                                                        1. Bellman Equations
                                                                                                                                                                                        2. Dynamic Programming
                                                                                                                                                                                          1. Policy Evaluation
                                                                                                                                                                                            1. Policy Improvement
                                                                                                                                                                                              1. Policy Iteration
                                                                                                                                                                                                1. Value Iteration
                                                                                                                                                                                                2. Temporal Difference Learning
                                                                                                                                                                                                  1. Q-Learning
                                                                                                                                                                                                    1. SARSA
                                                                                                                                                                                                      1. Expected SARSA
                                                                                                                                                                                                        1. Exploration vs. Exploitation
                                                                                                                                                                                                        2. Function Approximation
                                                                                                                                                                                                          1. Linear Function Approximation
                                                                                                                                                                                                            1. Neural Network Approximation
                                                                                                                                                                                                              1. Deep Q-Networks
                                                                                                                                                                                                              2. Policy Gradient Methods
                                                                                                                                                                                                                1. REINFORCE Algorithm
                                                                                                                                                                                                                  1. Actor-Critic Methods
                                                                                                                                                                                                                    1. Advantage Functions

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                                                                                                                                                                                                                3. Knowledge Representation and Reasoning

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                                                                                                                                                                                                                5. Neural Networks and Deep Learning