Contents (hardcover)

• Preface

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
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
3. Fundamental Algorithms
   • Linear Regression
   • Logistic Regression
   • Decision Tree Learning
   • Support Vector Machine
   • k-Nearest Neighbors
4. Anatomy of a Learning Algorithm
   • Building Blocks of a Learning Algorithm
   • Gradient Descent
   • How Machine Learning Engineers Work
   • Learning Algorithms' Particularities
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
6. Neural Networks and Deep Learning
   • Neural Networks
     • Multilayer Perceptron Example
     • Feed-Forward Neural Network Architecture
   • Deep Learning
     • Convolutional Neural Network
     • Recurrent Neural Network
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
8. Advanced Practice
   • Handling Imbalanced Datasets
   • Combining Models
   • Training Neural Networks
   • Advanced Regularization
   • Handling Multiple Inputs
   • Handling Multiple Outputs
   • Transfer Learning
   • Algorithmic Efficiency
9. 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
10. Other Forms of Learning
    • Metric Learning
    • Association Rule Learning
    • Learning to Rank
    • Learning to Recommend
      • Factorization Machines
      • Denoising Autoencoders
    • Self-Supervised Learning: Word Embeddings
11. Conclusion
    • Topic Modeling
    • Gaussian Processes
    • Generalized Linear Models
    • Probabilistic Graphical Models
    • Markov Chain Monte Carlo
    • Genetic Algorithms
    • Reinforcement Learning