🔍 Anomaly Detection Introduction Series v1.0

Learn implementation methods for anomaly detection in real-world data, from fundamentals of anomaly detection to statistical methods, machine learning, and deep learning-based anomaly detection techniques

Series Overview

This series is a practical educational content consisting of four chapters that allows you to systematically learn the theory and implementation of Anomaly Detection from fundamentals to advanced levels.

Anomaly Detection is a machine learning technology that identifies data points that deviate from normal patterns, playing a crucial role in various fields such as defect detection in manufacturing, fraud detection in finance, intrusion detection in cybersecurity, and early disease detection in healthcare. Starting with statistical approaches using the 3-sigma rule and outlier detection, we will systematically study diverse methods including machine learning-based Isolation Forest, One-Class SVM, deep learning-based Autoencoders, VAE, GAN, and even time series anomaly detection. Understanding the differences between unsupervised learning that trains only on normal data, semi-supervised learning that uses a small amount of abnormal data, and supervised learning that uses both labels, you will be able to select and implement appropriate methods according to actual business challenges. Through practical implementation using major libraries such as scikit-learn, PyTorch, and TensorFlow, you will acquire skills in building anomaly detection systems.

Features:

• ✅ From Theory to Practice: Systematic learning from fundamental concepts of anomaly detection to implementation and evaluation
• ✅ Implementation-Focused: Over 35 executable Python/scikit-learn/PyTorch code examples
• ✅ Diverse Methods: Wide range of approaches including statistical methods, machine learning, and deep learning
• ✅ Latest Technology Compliance: Comprehensive coverage of Autoencoders, VAE, GAN, and time series anomaly detection
• ✅ Practical Applications: Real-world application examples in manufacturing, finance, security, and healthcare

Total Learning Time: 4.5-5.5 hours (including code execution and exercises)

How to Learn

Recommended Learning Sequence

For Beginners (completely new to anomaly detection):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 (all chapters recommended)
- Time required: 4.5-5.5 hours

For Intermediate Learners (with machine learning experience):
- Chapter 2 → Chapter 3 → Chapter 4
- Time required: 3.5-4.5 hours

For Focused Topic Study:
- Anomaly detection fundamentals and evaluation metrics: Chapter 1 (focused study)
- Statistical methods and outlier detection: Chapter 2 (focused study)
- Machine learning-based methods: Chapter 3 (focused study)
- Deep learning and time series anomaly detection: Chapter 4 (focused study)
- Time required: 60-80 minutes/chapter

Chapter Details

Chapter 1: Fundamentals of Anomaly Detection

Difficulty: Intermediate
Reading Time: 60-70 minutes
Code Examples: 8

Learning Content

1. What is Anomaly Detection - Definition of anomalies, deviation from normal patterns
2. Types of Tasks - Unsupervised learning, semi-supervised learning, supervised learning
3. Application Areas - Manufacturing, finance, security, healthcare, IoT
4. Evaluation Metrics - Precision, recall, F1 score, ROC-AUC, PR-AUC
5. Challenges and Constraints - Class imbalance, lack of labels, real-time requirements

Learning Objectives

• ✅ Understand fundamental concepts of anomaly detection
• ✅ Explain types of anomaly detection tasks
• ✅ Select appropriate evaluation metrics
• ✅ Understand challenges of class imbalance
• ✅ Explain real-world applications of anomaly detection

Read Chapter 1 →

Chapter 2: Statistical Methods

Difficulty: Intermediate
Reading Time: 60-70 minutes
Code Examples: 9

Learning Content

1. 3-Sigma Rule - Normal distribution-based anomaly detection, mean and standard deviation
2. Interquartile Range (IQR) - Box plots, outlier detection
3. Mahalanobis Distance - Multivariate data anomaly detection, accounting for correlations
4. Statistical Hypothesis Testing - Grubbs test, Dixon test, outlier significance
5. Moving Average and Moving Standard Deviation - Time series data anomaly detection

Learning Objectives

• ✅ Detect anomalies using the 3-sigma rule
• ✅ Implement outlier detection using IQR
• ✅ Calculate Mahalanobis distance
• ✅ Apply statistical hypothesis testing
• ✅ Detect anomalies in time series data

Read Chapter 2 →

Chapter 3: Machine Learning-Based Anomaly Detection

Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 10

Learning Content

1. Isolation Forest - Anomaly detection through random isolation, handling high-dimensional data
2. LOF (Local Outlier Factor) - Local density-based anomaly scoring, neighborhood-based method
3. One-Class SVM - Learning normal data boundaries, kernel methods
4. DBSCAN - Density-based clustering, noise detection
5. K-Nearest Neighbors (KNN) - Distance-based anomaly detection, simple and effective

Learning Objectives

• ✅ Detect anomalies using Isolation Forest
• ✅ Detect local anomalies using LOF
• ✅ Implement One-Class SVM
• ✅ Identify noise using DBSCAN
• ✅ Understand characteristics and usage of each method

Read Chapter 3 →

Chapter 4: Deep Learning-Based Anomaly Detection

Difficulty: Intermediate to Advanced
Reading Time: 80-90 minutes
Code Examples: 11

Learning Content

1. Autoencoder - Reconstruction error-based anomaly detection, dimensionality reduction
2. VAE (Variational Autoencoder) - Probabilistic latent representations, generative models
3. GAN (Generative Adversarial Network) - AnoGAN, normal data generation
4. LSTM Autoencoder - Time series anomaly detection, sequential pattern learning
5. Transformer - Attention mechanism, capturing long-term dependencies

Learning Objectives

• ✅ Detect anomalies using Autoencoders
• ✅ Implement probabilistic anomaly detection using VAE
• ✅ Understand GAN-based anomaly detection
• ✅ Detect time series anomalies using LSTM Autoencoder
• ✅ Apply Transformers to anomaly detection

Read Chapter 4 →

Overall Learning Outcomes

Upon completing this series, you will acquire the following skills and knowledge:

Knowledge Level (Understanding)

• ✅ Explain fundamental concepts and types of anomaly detection tasks
• ✅ Understand characteristics of statistical methods, machine learning, and deep learning
• ✅ Explain advantages, disadvantages, and usage scenarios for each method
• ✅ Understand meaning and selection criteria for evaluation metrics
• ✅ Explain approaches to handling class imbalance problems

Practical Skills (Doing)

• ✅ Detect outliers using 3-sigma rule and IQR
• ✅ Implement Isolation Forest and LOF
• ✅ Learn normal patterns using One-Class SVM
• ✅ Detect anomalies using Autoencoders
• ✅ Implement time series anomaly detection

Application Ability (Applying)

• ✅ Select methods based on data characteristics
• ✅ Measure performance with appropriate evaluation metrics
• ✅ Handle class imbalance
• ✅ Design real-time anomaly detection systems
• ✅ Solve anomaly detection challenges in practical business contexts

Prerequisites

To effectively learn this series, it is desirable to have the following knowledge:

Required (Must Have)

• ✅ Python Fundamentals: Variables, functions, classes, NumPy, pandas
• ✅ Machine Learning Fundamentals: Concepts of training, validation, and testing
• ✅ Statistics Fundamentals: Mean, standard deviation, normal distribution
• ✅ scikit-learn Fundamentals: Model training and evaluation
• ✅ Data Visualization: matplotlib, seaborn

Recommended (Nice to Have)

• 💡 Deep Learning Fundamentals: Neural networks, gradient descent (for Chapter 4)
• 💡 PyTorch/TensorFlow: Deep learning frameworks (for Chapter 4)
• 💡 Time Series Analysis: ARIMA, moving averages (for time series anomaly detection)
• 💡 Dimensionality Reduction: PCA, t-SNE (for visualization)
• 💡 Clustering: K-means, DBSCAN (for Chapter 3)

Recommended Prior Learning:

• 📚 - ML fundamentals