- Differentiate AI-intensive systems from traditional software systems (K2): Students will describe how AI systems differ from conventional software systems in terms of design and engineering.
- Apply AI system life cycle principles (K3): Students will apply principles of the AI system life cycle, including requirements gathering, analysis, design, and integration.
- Work on a team-based challenge problem (K4): Students will analyze and tackle a practical challenge in a team, demonstrating collaboration and problem-solving skills.
- Implement ML classifiers for software systems (K5): Students will evaluate and implement machine learning classifiers, assessing their performance and efficiency in real-world scenarios.
- Integrate AI components effectively (K6): Students will develop and integrate various AI components into a cohesive and also traditional system, addressing compatibility and performance issues.
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- Engineering of AI-intensive Systems: Understanding the specific engineering practices and challenges associated with AI systems.
- AI System Life Cycle: Knowledge of the life cycle activities for AI systems, including requirements, analysis, design, and integration.
- Team-Based Problem Solving: Familiarity with collaborative approaches to solving complex problems in AI system design and implementation.
- Machine Learning Classifiers: Insight into implementing and optimizing machine learning classifiers within software systems.
- Integration of AI Components: Understanding the integration of different AI components into a unified system, ensuring compatibility and effectiveness.
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