Introduction to Synthetic Data for Machine Learning
Chapter 1: Understanding Synthetic Data
What is Synthetic Data?
Why Generate Artificial Data?
Real Data vs. Synthetic Data
Common Terminology
Potential Benefits
General Limitations
Quiz for Chapter 1
Chapter 2: Basic Methods for Data Generation
The Idea of Data Generation Models
Generating Data from Statistical Distributions
Introduction to Rule-Based Systems
Generating Simple Numerical Data
Generating Simple Categorical Data
Hands-on Practical: Create Basic Synthetic Data
Quiz for Chapter 2
Chapter 3: Generating Synthetic Tabular Data
Understanding Tabular Data Structure
Row Sampling Techniques
Independent Column Value Generation
Preserving Basic Column Correlations
Introduction to Data Anonymization Concepts
Hands-on Practical: Generate a Synthetic Table
Quiz for Chapter 3
Chapter 4: Introduction to Synthetic Image Data
Why Synthetic Data for Images?
Basic Image Properties: Pixels and Color
Creating Images with Simple Shapes and Patterns
Applying Noise and Simple Augmentations
Introduction to Rendering Simple Scenes
Challenges in Realistic Image Generation
Hands-on Practical: Generate Simple Synthetic Images
Quiz for Chapter 4
Chapter 5: Evaluating Synthetic Data Quality
Importance of Evaluation
Visual Inspection Methods
Basic Statistical Comparisons
Comparing Data Distributions
Concept of Fidelity vs. Utility
Quiz for Chapter 5
Chapter 6: Tools and Libraries Overview
Role of Software in Data Generation
Libraries for Basic Data Manipulation (NumPy, Pandas)
Introduction to Faker Library
Libraries for Simple Image Manipulation (Pillow, Scikit-image)
Finding Generation Tools
Quiz for Chapter 6
The Idea of Data Generation Models
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- Synthetic Data for Machine Learning: Principles and Practice, Maksim Khakimov, Altynbek Seitov, Aziza Alimzhanova, and Zhamilya Baizakova, 2021 (Springer) DOI: 10.1007/978-3-030-74697-3 - Provides a foundational introduction to synthetic data, defining its purpose and outlining basic generation principles, including statistical methods.
- A Survey on Synthetic Data Generation, Jingge Li, Xiangyu Han, Yaliang Li, Chengyu Song, Jun Ma, 2022 Journal of Big Data Analytics in Transportation, Vol. 4 (Springer) DOI: 10.1007/s42493-022-00078-0 - Offers a comprehensive overview of synthetic data generation methods, from basic statistical models to more complex machine learning approaches.
- Monte Carlo Statistical Methods, Christian P. Robert, George Casella, 2004 (Springer) DOI: 10.1007/978-0-387-21239-5 - A classic text detailing methods for generating random variates from various statistical distributions, fundamental to the "statistical descriptions" approach for synthetic data. (2nd edition)
- Privacy-Preserving Data Publishing, Raymond Wong, Min Wu, and Chengqi Zhang, 2011 (Springer) DOI: 10.1007/978-1-4419-5867-0 - Discusses techniques for publishing data while preserving privacy, including methods that involve generating synthetic or perturbed data based on statistical properties.