Topics Covered

• Predictive AI Business and Technology Drivers
• Predictive AI Benefits
• Common Risks and Challenges of Using Predictive AI
• Business Problem Categories Addressed by AI
• Types of Predictive AI
• Common Predictive AI Learning Approaches
• Understanding Predictive AI Learning and Model Training
• Step-by-Step Training Loop Process

• Supervised Learning, Unsupervised Learning, Continuous Learning
• Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
• Common Predictive AI Functional Designs, Computer Vision, Pattern Recognition
• Robotics, Natural Language Processing (NLP)
• Speech Recognition, Natural Language Understanding (NLU)
• Understanding AI Models and Neural Networks

Topics Covered

• Generative AI Business and Technology Drivers
• Generative AI Benefits
• Common Risks and Challenges of Using Generative AI
• Business Problem Categories Addressed by Generative AI
• Understanding Models, Algorithms and Neural Networks

• Types of Generative AI
• Understanding Generative Adversarial Networks (GANs)
• Understanding Variational Encoders (VAE)
• Understanding Transformers
• Steps to Building AI Systems
• Generative AI Best Practices

Topics Covered

• AI Concepts and Systems
• Understanding AI Agents
• Distinguishing Agentic AI from Other Types of AI
• Core Agent Characteristics (Autonomy, Reactivity, Proactiveness, Social Ability)
• The Concept of “Agency” and Its Implications in AI Systems
• Perception: Sensors and How Agents Gather Information
• Reasoning: Internal Processing and Decision-Making
• Action: Actuators and How Agents Interact with Their Environment

• The Agent-Environment Interaction Loop
• Basic Agent Architectures: Simple Reflex Agents, Model-Based Reflex Agents, Goal-Based Agents and Utility-Based Agents
• Introduction to Knowledge Representation (KR)
• Basic Planning Algorithms: State-Space Search and Goal Stack Planning
• Introduction to Decision Theory and Rational Agents
• Overview of Agent Types

Topics Covered

• Introduction to AI Governance and the Fundamental Principles of Ethical AI
• Understanding Fairness, Accountability, Transparency and Explainability (FATE) in Autonomous Systems
• Addressing Bias and Discrimination in AI Algorithms and Agentic Decision-Paths
• Understanding the role of Data Governance within an AI Governance Framework
• Governing the Collection and Management of Training Data
• Ensuring Data Quality and Tracking Data Lineage

• Techniques for Addressing and Mitigating Bias in Datasets
• Examining Ethical Considerations Throughout an AI System’s Lifecycle
• Understanding the Tools and Techniques Available for Ethical AI Design
• Utilizing Risk Assessment Frameworks to Identify and Manage Potential AI Risks
• Defining Agentic AI: Governance of Autonomy, Goal Alignment, and Recursive Reasoning

Topics Covered

• Governance Precepts and Processes for Training Data, including Sensitive Data and Representativeness
• Governance Precepts and Processes for Production Data, including Monitoring for Behavioral Drift and Continuous Fairness
• Establishing AI Oversight and Auditing Mechanisms for Multi-Step Agentic Workflows
• Bias Mitigation Strategies and Achieving Explainability in Complex AI Models
• Operationalizing Ethical Principles and Aligning AI with Values and Social Responsibility
• Adapting to Evolving Standards and Regulations

• Creating an AI Ethics Code of Conduct and Communicating AI Ethics to the Organization
• Using AI Governance to Foster a Culture of Ethical AI Innovation
• Proactive Risk Management and Incident Response for Autonomous Failures and Cascading Errors
• Understanding Governance-related Cloud AI Automation Mechanisms
• Governing Tool-Use and API Interaction: Security and Ethics of Agents in Digital Environments
• Human-in-the-Loop (HITL) vs. Human-on-the-Loop (HOTL): Establishing "Kill-Switches" and Intervention Thresholds

Topics Covered