AI 113 Understanding AI Systems

1. Explain the core components of artificial intelligence systems, including data, algorithms, and machine learning principles.
2. Describe how AI systems process information and make decisions, connecting conceptual models to real-world applications.
3. Use no-code or low-code AI tools to explore basic system design, model behavior, and data-driven outcomes.
4. Analyze AI outputs for accuracy, bias, and ethical considerations within simulated or guided exercises.
5. Differentiate between traditional programming logic and AI-based decision processes.
6. Demonstrate foundational technical understanding of how AI models are trained, refined, and deployed using simplified tools.
7. Evaluate the societal and professional impacts of AI technologies in various industries.

• Introduction to Artificial Intelligence Systems
  • Overview of AI technologies and their evolution
  • Core differences between traditional programming and AI systems
  • Key applications of AI across industries
  • Ethical foundations and responsible AI system design
• Data Foundations
  • Role of data in AI and machine learning
  • Data types, collection, and preprocessing
  • Training data vs. inference data
  • Bias in datasets and its effects on outcomes
• Algorithms and Machine Learning Concepts
  • What algorithms are and how they relate to AI
  • Supervised vs. unsupervised learning
  • Classification, clustering, and regression basics
  • Understanding model “training” and “inference”
• How AI Systems Process Information
  • The data pipeline: input → model → output
  • Decision-making logic in AI vs. traditional code
  • Neural networks and model architectures (conceptual)
  • Interpreting model confidence and probability
• Exploring AI Tools (No-Code and Low-Code Environments)
  • Introduction to user-friendly AI development tools (e.g., Teachable Machine, Lobe, Google AI Studio)
  • Building and testing simple models without programming
  • Understanding the model lifecycle (train, test, deploy)
  • Visualization and interpretation of model results
• Bias, Ethics, and Model Evaluation
  • Sources and impacts of algorithmic bias
  • Evaluating fairness, transparency, and accountability
  • Measuring accuracy and performance of models
  • Ethical considerations in real-world AI deployment
• AI in Context: Applications and Case Studies
  • AI in healthcare, finance, education, and business operations
  • Human-AI collaboration in decision-making
  • Emerging technologies: generative AI, robotics, predictive analytics
  • Discussion of societal and professional impacts

• Integrating Concepts

  • Review of conceptual and technical foundations
  • How AI models interact with data and automation systems
  • Connecting ethical, practical, and technical dimensions of AI
  • Reflection on the learner’s understanding of AI’s role in modern society
• Final Applied Project or Case Study
  • Analyze or design a simple AI use case using low-code tools
  • Demonstrate understanding of data flow, model behavior, and ethical implications
  • Present findings and reflect on outcomes and learning process