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nav:
  - Overview: index.ipynb
  - Topics:
      - Foundations:
          - "An Introduction to AI": chapters/0. Foundations/0. Introduction.md
          - "Definitions and Notation": chapters/0. Foundations/1. Definitions and Notation.md

          - Probability Theory:
              - "Set Theory": chapters/0. Foundations/2. Probability Theory/0. Set Theory.md
              - "Random Experiments": chapters/0. Foundations/2. Probability Theory/1. Random Experiments.md
              - "Expectation and Variance": chapters/0. Foundations/2. Probability Theory/2. Expectation and Variance.md
              - "Bayes' Theorem": chapters/0. Foundations/2. Probability Theory/3. Bayes Theorem.md

          - Statistical Inference:
              - "Point Estimation": chapters/0. Foundations/3. Statistical Inference/0. Point Estimation.md
              - "Bias vs Variance": chapters/0. Foundations/3. Statistical Inference/1. Bias vs Variance.md
              - "Covariance": chapters/0. Foundations/3. Statistical Inference/2. Covariance.md
              - "Correlation vs Causation": chapters/0. Foundations/3. Statistical Inference/3. Correlation vs Causation.md
              - "Confidence Intervals and Uncertainty": chapters/0. Foundations/3. Statistical Inference/4. CI and Uncertainty.md

          - Linear Algebra:
              - "Vectors and Matrices": chapters/0. Foundations/4. Linear Algebra/0. Vectors and Matrices.md
              - "Matrix Operations": chapters/0. Foundations/4. Linear Algebra/1. Matrix Operations.md
              - "Linear Transformations": chapters/0. Foundations/4. Linear Algebra/2. Linear Transformations.md
              - "Eigenvalues and Eigenvectors": chapters/0. Foundations/4. Linear Algebra/3. Eigenvalues and Eigenvectors.md
              - "Singular Value Decomposition (SVD)": chapters/0. Foundations/4. Linear Algebra/4. SVD.md

          - Calculus and Optimisation Basics:
              - "Gradients": chapters/0. Foundations/5. Calculus and Optimisation Basics/0. Gradients.md
              - "The Chain Rule": chapters/0. Foundations/5. Calculus and Optimisation Basics/1. Chain Rule.md
              - "Convexity": chapters/0. Foundations/5. Calculus and Optimisation Basics/2. Convexity.md
              - "Introduction to Gradient Descent": chapters/0. Foundations/5. Calculus and Optimisation Basics/3. Intro Gradient Descent.md
      
      - Data:
          - Introduction to Data: chapters/1. Data/0. introduction to data.md
          - Data Types and Structures:
              - "Tabular Data": chapters/1. Data/0. Data Types and Structures/0. Tabular Data.md
              - "Time Series Data": chapters/1. Data/0. Data Types and Structures/1. Time Series Data.md
              - "Image Data": chapters/1. Data/0. Data Types and Structures/2. Image Data.md
        
          - Exploratory Data Analysis (EDA): chapters\1. Data\1. Exploratory Data Analysis.md
        
          - Preprocessing:
              - "Data Cleaning": chapters/1. Data/2. Preprocessing/0. Data Cleaning.md
              - "Encoding Categorical Variables": chapters/1. Data/2. Preprocessing/1. Encoding Categorical Variables.md
              - "Feature Engineering": chapters/1. Data/2. Preprocessing/2. Feature Engineering.md
              - "Normalisation and Standardisation": chapters/1. Data/2. Preprocessing/3. Data Normalisation.md
              - "Dimensionality Reduction": chapters/1. Data/2. Preprocessing/4. Dimensionality Reduction.md

      - Statistical Learning Theory:
          - "Learning as Function Approximation": chapters/2. Statistical Learning Theory/0. Learning as Function Approximation.md
          - "Loss Functions and Risk Minimisation": chapters/2. Statistical Learning Theory/1. Loss Functions and Risk Minimisation.md
          - "Empirical Risk Minimisation": chapters/2. Statistical Learning Theory/2. Empirical Risk Minimisation.md
          - "Generalisation, Overfitting, and Underfitting": chapters/2. Statistical Learning Theory/3. OverUnder fitting.md
          - "Regularisation": chapters/2. Statistical Learning Theory/4. Regularisation.md
          - "Bias-Variance Tradeoff": chapters/2. Statistical Learning Theory/5. Bias-Variance Tradeoff.md
          - "No Free Lunch Theorem": chapters/2. Statistical Learning Theory/6. No Free Lunch Theorem.md
          - "Generalisation Gap": chapters/2. Statistical Learning Theory/7. Generalisation Gap.md
          - "VC Dimension": chapters/2. Statistical Learning Theory/8. VC Dimension.md
          - "PAC Learning": chapters/2. Statistical Learning Theory/9. PAC Learning.md

      - Supervised Learning:
          - "Supervised Learning Framework": chapters/3. Supervised Learning/0. Supervised Learning Framework.md

          - Classification Models:
              - "K-Nearest Neighbors (Classification)": chapters/3. Supervised Learning/1. Classification Models/0. KNN (C).md
              - "Decision Trees (Classification)": chapters/3. Supervised Learning/1. Classification Models/1. DT (C).ipynb
              - "Random Forests (Classification)": chapters/3. Supervised Learning/1. Classification Models/2. RF (C).ipynb
              - "Logistic Regression": chapters/3. Supervised Learning/1. Classification Models/3. LogReg.ipynb
              - "Support Vector Machines (SVM)": chapters/3. Supervised Learning/1. Classification Models/4. SVM.ipynb
              - "Naive Bayes Classifier": chapters/3. Supervised Learning/1. Classification Models/5. Naive Bayes.ipynb

          - Regression Models:
              - "Linear Regression": chapters/3. Supervised Learning/2. Regression Models/0. Linear Regression.md
              - "Polynomial Regression": chapters/3. Supervised Learning/2. Regression Models/1. Polynomial Regression.md
              - "Ridge and Lasso Regression": chapters/3. Supervised Learning/2. Regression Models/2. Ridge and Lasso.md

          - Evaluation Metrics:
              - "Confusion Matrix": chapters/3. Supervised Learning/3. Evaluation Metrics/0. Confusion Matrix.md
              - "Precision, Recall, and F1-Score": chapters/3. Supervised Learning/3. Evaluation Metrics/1. Precision Recall F1.md
              - "ROC Curve and AUC": chapters/3. Supervised Learning/3. Evaluation Metrics/2. ROC and AUC.md
          
          - Model Selection and Validation:
              - "Cross-Validation": chapters/3. Supervised Learning/4. Model Selection and Validation/0. Cross-Validation.md
              - "Hyperparameter Tuning": chapters/3. Supervised Learning/4. Model Selection and Validation/1. Hyperparameter Tuning.md
              - "Learning Curves": chapters/3. Supervised Learning/4. Model Selection and Validation/2. Learning Curves.md

      - Unsupervised Learning:
          - "Unsupervised Learning Framework": chapters/4. Unsupervised Learning/0. Unsupervised Learning Framework.md

          - Clustering Algorithms:
              - "k-Means Clustering": chapters/4. Unsupervised Learning/1. Clustering Algorithms/0. k-Means.md
              - "Hierarchical Clustering": chapters/4. Unsupervised Learning/1. Clustering Algorithms/1. Hierarchical Clustering.md
              - "DBSCAN": chapters/4. Unsupervised Learning/1. Clustering Algorithms/2. DBSCAN.md

          - Dimensionality Reduction Techniques:
              - "Principal Component Analysis (PCA)": chapters/4. Unsupervised Learning/2. Dimensionality Reduction Techniques/0. PCA.md
              - "Introduction to Kernel PCA": chapters/4. Unsupervised Learning/2. Dimensionality Reduction Techniques/1. Kernel PCA.md
              - "PCA with SVD": chapters/4. Unsupervised Learning/2. Dimensionality Reduction Techniques/2. PCA with SVD.md

          - Mixture Models:
              - "Gaussian Mixture Models (GMMs)": chapters/4. Unsupervised Learning/3. Mixture Models/0. GMMs.md
              - "Expectation-Maximisation (EM) Algorithm": chapters/4. Unsupervised Learning/3. Mixture Models/1. EM Algorithm.md
    
          - Evaluation Metrics for Unsupervised Learning:
              - "Silhouette Score": chapters/4. Unsupervised Learning/4. Evaluation Metrics for Unsupervised Learning/0. Silhouette Score.md
              - "Elbow Method": chapters/4. Unsupervised Learning/4. Evaluation Metrics for Unsupervised Learning/1. Elbow Method.md

      - Semi-Supervised Learning:
          - "Semi-Supervised Learning Framework": chapters/5. Semi-Supervised Learning/0. Semi-Supervised Learning Framework.md
          - "Self-Training": chapters/5. Semi-Supervised Learning/1. Self-Training.md
          - "Consistency Regularisation": chapters/5. Semi-Supervised Learning/2. Consistency Regularisation.md
          - "Graph-Based Methods": chapters/5. Semi-Supervised Learning/3. Graph-Based Methods.md

      - Learning Parametric Models:
          - "Parametric vs Nonparametric Models": chapters/6. Learning Parametric Models/0. Parametric vs Nonparametric Models.md
          - "Maximum Likelihood Estimation (MLE)": chapters/6. Learning Parametric Models/1. MLE.md
          - "Exponential Family Models": chapters/6. Learning Parametric Models/2. Exponential Family Models.md
          - "Generalised Linear Models (GLMs)": chapters/6. Learning Parametric Models/3. GLMs.md
          - "Second order gradient methods": chapters/6. Learning Parametric Models/4. Second Order Gradient Methods.md

      - Artificial Neural Networks:
          - "The Perceptron": chapters/7. Artificial Neural Networks/0. The Perceptron.md
          - "Multi-layer Perceptrons (MLPs)": chapters/7. Artificial Neural Networks/1. Multi-layer Perceptrons.md
          - "Activation Functions": chapters/7. Artificial Neural Networks/2. Activation Functions.md
          - "Backpropagation": chapters/7. Artificial Neural Networks/3. Backpropagation.md
          - "Optimisation Algorithms": chapters/7. Artificial Neural Networks/4. Optimisation Algorithms.md
          - "Regularisation": chapters/7. Artificial Neural Networks/5. Regularisation.md

      - Deep Learning: 
          - "Representation Learning": chapters/8. Deep Learning/0. Representation Learning.md
          - "Convolutional Neural Networks (CNNs)": chapters/8. Deep Learning/1. CNNs.md
          - "Recurrent Neural Networks (RNNs)": chapters/8. Deep Learning/2. RNNs.md
          - "Sequence Models": chapters/8. Deep Learning/3. Sequence Models.md
          - "Transformers": chapters/8. Deep Learning/4. Transformers.md
          - "Training Deep Networks": chapters/8. Deep Learning/5. Training Deep Networks.md

      - Ensemble Methods:
          - "Bagging and Bootstrap Aggregation": chapters/9. Ensemble Methods/0. Bagging and Bootstrap Aggregation.md
          - "Boosting Algorithms": chapters/9. Ensemble Methods/1. Boosting Algorithms.md

      - Kernel Methods:
          - "The Kernel Trick": chapters/10. Kernel Methods/0. Kernel Functions.md
          - "Support Vector Machines (SVMs) with Kernels": chapters/10. Kernel Methods/1. SVMs with Kernels.md
          - "Kernel Ridge Regression": chapters/10. Kernel Methods/2. Kernel Ridge Regression.md
          - "Kernel PCA": chapters/10. Kernel Methods/3. Kernel PCA.md

      - Gaussian Processes:
          - "Introduction to Gaussian Processes": chapters/11. Gaussian Processes/0. Introduction to Gaussian Processes.md
          - "Nonparametric Bayesian Modelling": chapters/11. Gaussian Processes/1. Nonparametric Bayesian Modelling.md
          - "Covariance Functions and Kernels": chapters/11. Gaussian Processes/2. Covariance Functions and Kernels.md
          - "Gaussian Process Regression": chapters/11. Gaussian Processes/3. Gaussian Process Regression.md
          - "Gaussian Process Classification": chapters/11. Gaussian Processes/4. Gaussian Process Classification.md

      - Generative Models:
          - "Generative vs Discriminative Models": chapters/12. Generative Models/0. Generative vs Discriminative Models.md
          - "Latent Variable Models": chapters/12. Generative Models/1. Latent Variable Models.md
          - "Variational Autoencoders (VAEs)": chapters/12. Generative Models/2. VAEs.md
          - "Generative Adversarial Networks (GANs)": chapters/12. Generative Models/3. GANs.md
          - "Normalising Flows": chapters/12. Generative Models/4. Normalising Flows.md

      - Performance:
          - "Model Evaluation: Robustness & Stability": chapters/13. Performance/0. Model Evaluation Robustness and Stability.md
          - "Calibration": chapters/13. Performance/1. Calibration.md
          - "Fairness, Bias, and Ethics": chapters/13. Performance/2. Fairness Bias and Ethics.md
          - "Computational Efficiency": chapters/13. Performance/3. Computational Efficiency.md