This course provides hands-on training in applying machine learning techniques to audit analytics. Learners will explore how AI can automatically detect risks, identify anomalies, and group similar data through clustering.
The course begins with key machine learning concepts and workflows, then moves into practical exercises using unsupervised learning for outlier detection, supervised learning for risk prediction, and clustering for behavior analysis—building essential skills for intelligent, AI-driven auditing.

Learning Objectives

By completing this course, learners will be able to:

1. Understand the structure and workflow of machine learning in audit applications
2. Apply unsupervised learning to detect outliers and unusual behavior in data
3. Build supervised learning models for risk prediction and classification
4. Use clustering techniques to identify transaction patterns and group characteristics
5. Strengthen data-driven decision-making capabilities for intelligent auditing

By the end of the course, learners will be able to use machine learning to detect risks, predict anomalies, and classify data in audit tasks.

Table of Contents

1. Introduction and Workflow of Machine Learning
2. Supervised Machine Learning – Hands-on Practice: Learning and Prediction
3. Supervised Machine Learning – Machine Learning Algorithms
4. Unsupervised Machine Learning – Hands-on Practice: Outlier Detection Techniques
5. Unsupervised Machine Learning – Hands-on Practice: Clustering