About this course:
Course Introduction
The purpose of the session is to get the participants to understand the basics of AI / ML that is required to assess the new associated risks. The course has absolutely no prerequisites and gradually exposes participants to introductory and more advanced topics of AI / ML.
Learning Outcomes
The session will cover difference simple concepts around AI and Machine learning that with help the internal auditor understand their role in the innovation space along with some basic guidance in equipping them to consider risk elements arising out of AI and ML implementation in the organization.
During the session, practical scenarios will be discussed that will allow the participants to be able to apply basic concepts.
Course Schedule
- Day 1: 09:00 – 13:00
- Day 2: 09:00 – 13:00
- Day 3: 09:00 – 13:00
Who Should Attend
The course is designed for all the levels of the internal audit department that require a look into the future of the internal auditing processes within the AI and ML environment.
Course Outline
Understanding Machine Learning
- What is Machine Learning?
- How is machine learning different from AI
- Defining Big Data
- Role of Statistics and Data Mining in Machine Learning
- Approaches to machine learning?
How to use Machine Learning
- Devising a strategy
- Understanding basic techniques
- Applying Machine Learning to Internal Auditor Needs
Getting started with Machine Learning
- Impact of Machine Learning on Applications
- Focusing on the problem
- Things to consider in a pilot
- Considerations on a learning model
Using machine learning to provide solutions to business problems
- Auditing around Machine Learning
- What are the AI/ML Challenges, Opportunities and Risks?
- Machine learning and controls
- The role of the internal auditor in an ML /AI environment
Machine learning and the way forward with examples in different industries
With the conclusion for the session, the participants will be able to have a better understanding on the concepts and application in an internal audit environment.
