ARTIFICIAL INTELLIGENCE (AI) IN SYSTEMS

Prerequisites & course information

Requisites: ( MATH/STAT 309 , STAT 311 , MATH/STAT 431 or MATH 531 ) and ( I SY E 210 , E C E 331 , STAT/MATH 310 , STAT 312 , 324 , or 371 ), or graduate/professional standing Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Explain the basic principles and techniques of artificial intelligence. Audience: Undergraduate 2. Evaluate AI systems using various metrics and methodologies. Audience: Undergraduate 3. Represent engineered systems using mathematical models. Audience: Undergraduate 4. Assess the reliability and robustness of AI systems in complex industrial environments. Audience: Undergraduate 5. Analyze privacy concerns and ethical considerations in AI applications in engineering. Audience: Undergraduate 6. Discuss the importance of transparency and interpretability in AI systems. Audience: Undergraduate 7. Apply AI techniques to solve real-world problems involving engineered systems in complex industrial environments. Audience: Undergraduate Learning Outcomes: 1. Explain the basic principles and techniques of artificial intelligence. Audience: Undergraduate 2. Evaluate AI systems using various metrics and methodologies. Audience: Undergraduate 3. Represent engineered systems using mathematical models. Audience: Undergraduate 4. Assess the reliability and robustness of AI systems in complex industrial environments. Audience: Undergraduate 5. Analyze privacy concerns and ethical considerations in AI applications in engineering. Audience: Undergraduate 6. Discuss the importance of transparency and interpretability in AI systems. Audience: Undergraduate 7. Apply AI techniques to solve real-world problems involving engineered systems in complex industrial environments. Audience: Undergraduate