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Course Outline

Introduction to Artificial Intelligence

  • Definition of Artificial Intelligence
  • Critical milestones in the evolution of AI technology
  • Distinguishing Artificial Intelligence from Machine Learning and Deep Learning
  • Categorizations of AI: Narrow AI, General AI, and Superintelligent AI

Fundamental Concepts of Artificial Intelligence

  • The roles of data, algorithms, and models
  • Core principles of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • Foundations of neural networks and Deep Learning
  • Overview of Natural Language Processing (NLP)

Government Applications of Artificial Intelligence

  • Utilization of AI in healthcare, finance, retail, and transportation sectors
  • Deployment of intelligent virtual assistants and chatbot services for public use
  • Enhancing business analytics and decision-making processes with AI capabilities for government

Data Preparation for Artificial Intelligence Systems

  • Ensuring data quality and executing preprocessing protocols
  • Differentiating between structured and unstructured data sources
  • Addressing data ethics and mitigating bias within datasets
  • Methodologies for data collection and labeling

Ethical Standards and Governance in Artificial Intelligence

  • Ethical considerations during AI development lifecycle
  • Mitigating bias in AI models and underlying algorithms
  • Regulatory frameworks and governance structures applicable to AI for government
  • Ensuring accountability and transparency in AI systems

Artificial Intelligence Tools and Technological Infrastructure

  • Survey of widely adopted AI development frameworks
  • Introduction to leading AI platforms (Google AI, Microsoft Azure, IBM Watson)
  • Principles of automation and Robotic Process Automation (RPA)

Risks, Security Protocols, and Challenges in Artificial Intelligence

  • Identifying security vulnerabilities within AI systems
  • Mitigating risks associated with excessive reliance on automated systems
  • Assessing the socioeconomic impacts of AI adoption across public services
  • Maintaining AI model performance and implementing continuous monitoring

BCS Examination Preparation and Practice

  • Overview of the BCS examination format and structure
  • Sample questions and practice assessments
  • Identification of key focus areas for examination readiness
  • Strategic recommendations for final preparation

Summary and Future Directives

Requirements

  • No prior qualifications necessary

Target Participants

  • Information technology specialists
  • Business analysts
  • Project managers
 7 Hours

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