Course Details
Course Code: AIF; Course Duration: 3 days; / 21 hours; Instructor-led/ remote online training
The EXIN BCS Artificial Intelligence Foundation certification provides a comprehensive introduction to Artificial Intelligence (AI), focusing on its terminology, general principles, and practical applications. This course explores AI’s role in ‘Universal Design’ and the ‘Fourth Industrial Revolution,’ emphasizing ethical considerations, human-machine collaboration, and the development of machine learning toolsets.
Audience
This certification is designed for individuals interested in implementing AI within their organizations, particularly those in fields such as science, engineering, knowledge engineering, finance, education, or IT services.
Prerequisites
No prerequisite.
Methodology
This course is highly interactive and uses diverse teaching methods (lectures, self-assessments, group discussions, activities, and videos) to accommodate different learning styles This training will also leverage on participant’s personal life and work experiences.
Course Objectives
Upon completion of this course, participants will be able to:
- Describe how AI integrates into ‘Universal Design’ and the ‘Fourth Industrial Revolution.’
- Understand the concept of intelligent agents within AI.
- Explain the benefits and challenges associated with AI.
- Comprehend data learning processes, including relevant software and hardware functionalities.
- Recognize the dynamics of human-machine collaboration, particularly through machine learning.
- Describe a ‘Learning from Experience’ Agile Approach to Projects.
Outlines
Module 1: Introduction to AI and Historical Development
- Key definitions of Artificial Intelligence (AI) terminology
- Key milestones in the development of AI
- Types of AI
- Societal impact of AI
- Sustainability measures to reduce AI’s environmental impact
Module 2: Ethical and legal considerations
- Aligning AI with business goals
- Strategic planning for AI initiatives
Module 3: Enablers of AI
- Common examples of AI applications
- Role of robotics in AI systems
- Machine learning
- Common machine learning concepts
- Supervised and unsupervised learning processes
Module 4: Finding and using data in AI
- Key data terms relevant to AI
- Data quality characteristics and their importance in AI
- Risks associated with data handling and strategies to mitigate them
- Purpose and use of big data
- Data visualization techniques and tools
- Key generative AI terms
- Purpose and application of generative AI, including large language models
- Role of data in training AI within the machine learning process
Module 5: Using AI in your organisation
- Opportunities for implementing AI in an organization.
- Contents and structure of a business case
- Stakeholders for an AI project
- Project management approaches
- Risks, costs, and benefits associated with a proposed solution
- Ongoing governance activities required when implementing AI
Module 6: Future planning and impact – Human plus machine
- Roles and career opportunities emerging with AI
- Real-world applications of AI
- AI’s impact on society and the future of AI
- consciousness and its impact on ethical AI
