- Define the scope, disciplines, and systems of Artificial Intelligence (AI) to establish a foundational understanding of key concepts and terminology.
- Develop AI-ready data strategies that integrate diverse data types, ensure compliance with data governance standards, and manage the data lifecycle effectively.
- Analyze the components of the AI ecosystem, including model architectures and generative-based models.
- Illustrate the complete lifecycle of AI models from problem definition through model development, evaluation, and deployment.
- Apply effective prompt engineering strategies to optimize interactions with generative AI tools for tasks such as content generation, data analysis, and problem-solving.
- Evaluate AI reliability, security, and privacy by assessing performance metrics, detecting and mitigating hallucinations, and implementing risk mitigation strategies.
- Integrate responsible and ethical AI principles into all stages of model development and usage to ensure fairness, transparency, and accountability.
- Assess the societal, workforce, and environmental impacts of AI technologies to anticipate potential risks, benefits, and opportunities for innovation.
This introductory course provides a comprehensive foundation in Artificial Intelligence (AI), empowering students to work confidently with AI tools, systems, and methodologies across diverse professional settings. They will explore the scope and key disciplines of AI, including data strategies, model architectures, and the complete AI lifecycle. Through activities, students will apply prompt engineering techniques to enhance content generation, data analysis, and problem-solving. They will also integrate responsible AI principles into their work and be equipped to analyze the societal and workforce impacts of AI technologies across different contexts. By engaging with real-world scenarios and applied projects, students will learn to design, assess, and adapt AI solutions that are effective, fair, and sustainable.