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Association Rule Learning

Foreword
- 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
- 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
- Fundamental Algorithms
  * Linear Regression
  * Logistic Regression
  * Decision Tree Learning
  * Support Vector Machine
  * k-Nearest Neighbors
- Anatomy of a Learning Algorithm
  * Building Blocks of a Learning Algorithm
  * Gradient Descent
  * How Machine Learning Engineers Work
  * Learning Algorithms' Particularities
- 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
- Neural Networks and Deep Learning
  * Neural Networks
    * Multilayer Perceptron Example
    * Feed-Forward Neural Network Architecture
  * Deep Learning
    * Convolutional Neural Network
    * Recurrent Neural Network
- 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
- Advanced Practice
  * Handling Imbalanced Datasets
  * Combining Models
  * Training Neural Networks
  * Advanced Regularization
  * Handling Multiple Inputs
  * Handling Multiple Outputs
  * Transfer Learning
- 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