Applied Artificial Intelligence for Decision Making

Introduction

This course provides hands-on experience in applying artificial intelligence techniques to enhance decision-making processes in professional settings.

Objectives

• Understand core concepts of artificial intelligence and its relevance to decision making.
• Identify and analyze real-world applications of AI in decision processes.
• Develop skills to implement AI-driven decision support systems.
• Evaluate ethical and practical considerations in AI deployment.
• Leverage data to improve decision outcomes using AI tools.

Course Outlines

Day 1: Foundations of AI and Decision Making

• Overview of artificial intelligence and its evolution.
• Understanding decision-making frameworks.
• Types of decisions and role of data.
• Introduction to machine learning concepts.
• Identifying opportunities for AI in business decisions.

Day 2: Data, Algorithms, and Models

• Data collection and preprocessing for AI.
• Overview of machine learning algorithms.
• Supervised vs. unsupervised learning in decision contexts.
• Evaluating and selecting appropriate models.
• Hands-on: Building a simple prediction model.

Day 3: AI Applications in Decision Support

• Case studies of AI-driven decision support systems.
• Designing AI solutions for business challenges.
• Integrating AI with existing processes.
• Human-AI collaboration in the workplace.
• Practical exercise: Prototyping a decision support tool.

Day 4: Ethics, Bias, and Responsible AI

• Understanding ethical considerations in AI.
• Identifying and mitigating algorithmic bias.
• Transparency and explainability in AI decisions.
• Regulatory frameworks and compliance.
• Group discussion: Responsible AI adoption strategies.

Day 5: Implementation and Future Trends

• Deployment strategies for AI solutions.
• Scaling AI for organizational impact.
• Measuring effectiveness of AI-driven decisions.
• Emerging AI trends in decision making.
• Capstone project: Presenting your AI decision solution.