CISC121 Introduction to Artificial Intelligence and Machine Learning (Experimental)
Department of Science, Technology, Engineering & Mathematics: Computer/Information Science
- I. Course Number and Title
- CISC121 Introduction to Artificial Intelligence and Machine Learning (Experimental)
- II. Number of Credits
- 3 credits
- III. Number of Instructional Minutes
- 2250
- IV. Prerequisites
- None
- Corequisites
- None
- V. Other Pertinent Information
- This course provides an accessible introduction to the core ideas, techniques, and real-world applications of Artificial Intelligence (AI) and Machine Learning (ML). The course emphasizes conceptual understanding, hands-on exploration, and critical thinking rather than programming.
- VI. Catalog Course Description
- This course introduces foundational concepts in artificial intelligence and machine learning, including problem solving, search, classification, clustering, neural networks, and generative AI. Students examine how intelligent systems learn, make decisions, and generate content while evaluating real-world applications, ethical considerations, and societal impacts of AI across diverse fields.
- VII. Required Course Content and Direction
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Course Learning Goals
Students will:
- explain fundamental concepts in artificial intelligence and machine learning using correct terminology,
- describe how machines learn from data, including classification, clustering, regression, and reinforcement learning,
- interpret outputs of machine learning models and identify their strengths and limitations,
- engage with AI tools using no-code or low-code platforms to explore real-world applications,
- analyze ethical and societal implications of AI, including bias, privacy, transparency, and workforce impact, and
- evaluate claims about AI in media, products, and policy discussions with an informed and critical perspective.
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Planned Sequence of Topics and/or Learning Activities
- What is AI? History, myths, and reality
- Intelligent agents and problem solving
- Search, optimization, and decision-making
- Data, features, and learning from examples
- Classification and clustering
- Regression and prediction
- Neural networks and deep learning
- Reinforcement learning and AI in games
- Generative AI and Large Language Models
- AI in industry: health, business, media, and education
- Bias, fairness, privacy, and ethics in AI
- The future of AI: opportunities, risks, and careers
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Assessment Methods for Course Learning Goals
The assessment of Course Learning Goals is based on written tests, labs and other assignments, as well as performance-based tasks as appropriate. -
Reference, Resource, or Learning Materials to be used by Student:
See course syllabus
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New: 4/2026