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May 08, 2026
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AAIT 270 - Applied AI Projects
PREREQUISITES: AAIT 216 - Natural Language Processing
CREDIT HOURS MIN: 3
LECTURE HOURS MIN: 3
TOTAL CONTACT HOURS MIN: 48
This project-based course provides students with hands-on experience designing, developing, and deploying solutions using artificial intelligence (AI) and machine learning (ML) techniques. Students will work in teams to complete a substantial project that applies AI methods such as supervised learning, unsupervised learning, deep learning, natural language processing, and reinforcement learning. Through iterative development, students will practice critical aspects of the AI lifecycle including problem definition, data collection and preprocessing, model training and evaluation, ethical considerations, and deployment strategies. Emphasis is placed on project planning, documentation, ethical and responsible AI practices, and presenting outcomes to both a technical and nontechnical audience. This course prepares students to demonstrate professional-level AI competencies aligned with workforce expectations.
MAJOR COURSE LEARNING OBJECTIVES: Upon successful completion of this course the student will be expected to:
- Identify real-world problems that can be addressed using AI techniques.
- Design end-to-end AI systems, including data preparation, model selection, training, and evaluation.
- Collaborate effectively within a team to manage and execute a substantial AI project.
- Apply appropriate tools, frameworks, and techniques for AI development and deployment.
- Demonstrate ethical and responsible use of AI technologies, considering fairness, bias, and interpretability.
- Develop project goals, methodologies, and results for delivery to all stakeholders.
- Critique project outcomes and performance metrics to identify areas for improvement and future work.
COURSE CONTENT: Topical areas of study include -
- Problem Identification and Solution Design for AI Applications
- Data Collection, Preparation, and Feature Engineering
- Model Development, Training, and Integration
- Evaluation, Testing, and Performance Metrics
- Deployment Strategies and Lifecycle Management
- Natural Language Processing Applications
- Ethical and Responsible Implementation Practices
- Tools and Frameworks for AI Development and Deployment
- Team Collaboration, Project Management, and Documentation
- Technical Communication and Presentation of Project Results
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