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- | {{keywords>Association Rule Learning}} | + | {{keywords>contents}} |
<title classes #id> | <title classes #id> | ||
- | Association Rule Learning | + | Contents (hardcover) |
</title> | </title> | ||
+ | * [[http://bit.ly/theMLbook-Preface|Preface]] | ||
+ | - [[http://bit.ly/theMLbook-Chapter-1|Introduction]] | ||
+ | * What is Machine Learning | ||
+ | * Types of Learning | ||
+ | * Supervised Learning | ||
+ | * Unsupervised Learning | ||
+ | * Semi-Supervised Learning | ||
+ | * Reinforcement Learning | ||
+ | * How Supervised Learning Works | ||
+ | * Why the Model Works on New Data | ||
+ | - [[http://bit.ly/theMLbook-Chapter-2|Notation and Definitions]] | ||
+ | * Notation | ||
+ | * Scalars, Vectors, and Sets | ||
+ | * Capital Sigma Notation | ||
+ | * Capital Pi Notation | ||
+ | * Operations on Sets | ||
+ | * Max and Arg Max | ||
+ | * Operations on Vectors | ||
+ | * Functions | ||
+ | * Assignment Operator | ||
+ | * Derivative and Gradient | ||
+ | * Random Variable | ||
+ | * Classification vs Regression | ||
+ | * Instance-Based vs Model-Based Learning | ||
+ | * Shallow vs Deep Learning | ||
+ | - [[http://bit.ly/theMLbook-Chapter-3|Fundamental Algorithms]] | ||
+ | * Linear Regression | ||
+ | * Logistic Regression | ||
+ | * Decision Tree Learning | ||
+ | * Support Vector Machine | ||
+ | * k-Nearest Neighbors | ||
+ | - [[http://bit.ly/theMLbook-Chapter-4|Anatomy of a Learning Algorithm]] | ||
+ | * Building Blocks of a Learning Algorithm | ||
+ | * Gradient Descent | ||
+ | * How Machine Learning Engineers Work | ||
+ | * Learning Algorithms' Particularities | ||
+ | - [[http://bit.ly/theMLbook-Chapter-5|Basic Practice]] | ||
+ | * Feature Engineering | ||
+ | * One-Hot Encoding | ||
+ | * Binning | ||
+ | * Normalization | ||
+ | * Standardization | ||
+ | * Dealing With Missing Features | ||
+ | * Data Imputation Techniques | ||
+ | * Learning Algorithm Selection | ||
+ | * Three Sets | ||
+ | * Underfitting and Overfitting | ||
+ | * Regularization | ||
+ | * Model Performance Assessment | ||
+ | * Confusion Matrix | ||
+ | * Accuracy | ||
+ | * Cost-sensitive accuracy | ||
+ | * Precision/Recall | ||
+ | * Area under the ROC Curve (AUC) | ||
+ | * Hyperparameter Tuning | ||
+ | * Cross-Validation | ||
+ | - [[http://bit.ly/theMLbook-Chapter-6|Neural Networks and Deep Learning]] | ||
+ | * Neural Networks | ||
+ | * Multilayer Perceptron Example | ||
+ | * Feed-Forward Neural Network Architecture | ||
+ | * Deep Learning | ||
+ | * Convolutional Neural Network | ||
+ | * Recurrent Neural Network | ||
+ | - [[http://bit.ly/theMLbook-Chapter-7|Problems and Solutions]] | ||
+ | * Kernel Regression | ||
+ | * Multiclass Classification | ||
+ | * One-Class Classification | ||
+ | * Multi-Label Classification | ||
+ | * Ensemble Learning | ||
+ | * Random Forest | ||
+ | * Gradient Boosting | ||
+ | * Learning to Annotate Sequences | ||
+ | * Sequence-to-Sequence Learning | ||
+ | * Active Learning | ||
+ | * Semi-Supervised Learning | ||
+ | * One-Shot Learning | ||
+ | * Zero-Shot Learning | ||
+ | - [[http://bit.ly/theMLbook-Chapter-8|Advanced Practice]] | ||
+ | * Handling Imbalanced Datasets | ||
+ | * Combining Models | ||
+ | * Training Neural Networks | ||
+ | * Advanced Regularization | ||
+ | * Handling Multiple Inputs | ||
+ | * Handling Multiple Outputs | ||
+ | * Transfer Learning | ||
+ | * Algorithmic Efficiency | ||
+ | - [[http://bit.ly/theMLbook-Chapter09|Unsupervised Learning]] | ||
+ | * Density Estimation | ||
+ | * Clustering | ||
+ | * K-means Clustering | ||
+ | * DBSCAN and HDBSCAN | ||
+ | * Determining the Number of Clusters | ||
+ | * Other Clustering Algorithms | ||
+ | * Dimensionality Reduction | ||
+ | * Principal Component Analysis | ||
+ | * UMAP | ||
+ | * Outlier Detection | ||
+ | - Other Forms of Learning | ||
+ | * Metric Learning | ||
+ | * Association Rule Learning | ||
+ | * Learning to Rank | ||
+ | * Learning to Recommend | ||
+ | * Factorization Machines | ||
+ | * Denoising Autoencoders | ||
+ | * Self-Supervised Learning: Word Embeddings | ||
+ | - Conclusion | ||
+ | * Topic Modeling | ||
+ | * Gaussian Processes | ||
+ | * Generalized Linear Models | ||
+ | * Probabilistic Graphical Models | ||
+ | * Markov Chain Monte Carlo | ||
+ | * Genetic Algorithms | ||
+ | * Reinforcement Learning |