Course structure
Section 1: AI Fundamentals
1. Applications of AI and their benefits
Describes a range of applications of AI, how they impact organisations & society, what value they create and their underlying use of data, algorithms and learning approaches. Describes the role of the NL AIC in promoting the beneficial and ethical use of AI. Includes examples of key domains such as classification systems, forecasting, cluster analysis, voice, image and natural language processing.
2. Data, Robots and Artificial Intelligence including definitions
This sets out a common vocabulary around data, data science, algorithms, human logical thinking versus intelligent agents and provides definitions for key items. Describes Intelligent Agent types, robotics and agent models.
3. Predictions, Algorithms, Machine and Deep Learning
Introduces the different levels of predictions, an overview of the key algorithms and the learning approaches. Highlights which types of algorithms address which types of problems.
Section 2: Applying AI in practice
4. Building and assessing an AI application
Describes a basic approach towards building a simple AI application. The CRISP-DM method is described highlighting the steps involved and raising awareness of the business context and trustworthiness assessment at each step. Highlight pitfalls including overfitting, underfitting and bias. Addresses need for innovation and creativity including team organisation.
5. Managing Data for AI
Raises awareness of the dependence upon data and how to acquire, prepare, manage and provide and scale data for AI applications. Addresses the role of the cloud for managing data and processing capability. Emphasise the risks that arise in data and impact on trustworthiness.
Section 3: Ethics, Trustworthiness and Human Machine Coexistence
6. Ethics, Risks and Trustworthiness
Addresses the risks and ethical dilemmas associated with AI including the need for explainable AI. Introduce EU Ethical Guidelines and the need to maintain the trust of society in the use of AI.
7. Human and Machine Coexistence
Covers the combination of human and machine capability in an organisation addressing question of whether AI will replace humans (singularity). Includes key roles of business management, domain expertise, analytics and data managers, and how these roles work together.
Section 4: Future developments of AI
8. The future developments of AI
Highlight future directions and applications of AI.
