AI in Practice – Applying AI

Course Description

"AI in Practice – Applying AI" is an intermediate-level course designed for professionals, managers, and students interested in integrating Artificial Intelligence into their organizations. This course, part of the 'AI in Practice' two-course program, focuses on the practical aspects of implementing AI applications across various sectors. Instead of delving into complex algorithms and programming, it provides real-world examples and case studies from organizations such as ING, Radboud UMC, Municipality of Amsterdam, Ahold Delhaize, and KPN.

What Students Will Learn

• Describe the benefits and challenges of implementing AI in organizations
• Identify conditions and requirements for AI implementation
• Understand implementation aspects of AI and their significance for your organization
• Write a plan for applying AI in your own organization
• Examine typical AI applications already in use and learn from their experiences
• Explore challenges of implementation, lifecycle aspects, and maintenance of AI applications

Prerequisites

This course is designed for everyone, and no complicated math or programming expertise is required. It is suitable for those with an intermediate level of understanding in business and management concepts.

Course Content

• Reinforcement Learning for Real life (AI for FinTech Research)
• Self-Learning Forecasting in Retail
• Diagnostic Image Analysis for COVID-19
• AI Strategy and Implementation Aspects
• Agent Architecture of the Intake (Police AI)
• AI for Society (Civic AI)

Who This Course Is For

• Managers who want to understand AI's potential for their companies
• Data analysts and consultants looking to apply AI in business processes
• Students interested in translating AI research into practical applications
• Professionals from various sectors (healthcare, finance, retail, telecommunications, etc.)
• Anyone interested in learning about AI implementation without extensive technical knowledge

Real-World Applications

The skills acquired in this course will enable learners to:

1. Assess the potential of AI applications for their organizations
2. Develop strategies for implementing AI solutions
3. Understand and address challenges in AI integration
4. Create implementation plans tailored to their organization's needs
5. Evaluate the impact of AI on various business processes
6. Make informed decisions about AI adoption and investment
7. Collaborate effectively with AI specialists and development teams

Syllabus

1. Reinforcement Learning for Real life
   • AI for FinTech Research (ING and Delft University of Technology)
   • Bonus Track: Self-Learning Forecasting in Retail (Ahold Delhaize and University of Amsterdam)
2. Diagnostic Image Analysis for COVID-19
   • Thira Lab (Thirona and RadboudUMC)
3. Thematic Track on AI Strategy and Implementation Aspects of AI
   • Various labs (Vrije Universiteit Amsterdam, Dutch National Police, Elsevier, and Delft University of Technology)
4. Agent Architecture of the Intake
   • Police AI Lab Utrecht (University Utrecht and the Dutch National Police)
5. AI for Society
   • Civic AI Lab (Municipality of Amsterdam, University of Amsterdam, and Vrije Universiteit Amsterdam)

Each module features guest lecturers from ICAI labs, industry, and academia, providing diverse perspectives on AI implementation.