A Survey of AI: Transforming Our Lives
- Overview
- Course Content
- Requirements & Materials
COURSE ID: DEF 1100P
Contact for course-related questions
Renita FoldsInstructors
Margarita Gonzalez Renita Folds Clayton KerceA Survey of AI: Transforming Our Lives
Course Description
This comprehensive course provides a practical framework for understanding how Artificial Intelligence systems enter real-world operations. Rather than focusing solely on technical algorithms, you'll learn how AI models become reliable components, how components compose into systems, how systems integrate into human workflows, and how assurance is maintained throughout deployment. Organized around five pillars — Foundations, Componentization, Composition, Integration, and Assurance — you'll gain the operational competence needed to design, deploy, and oversee AI systems effectively in production environments. Three integrated threads (multimodal AI, interpretability, and classical-to-modern evolution) provide continuity across all topics.
1. Foundations
- AI system capabilities: deep learning, generation, reasoning, and multimodal models
- Digital transformation framing and organizational AI readiness
- Classical systems and the foundation model paradigm shift
- Vision-language-action models and contrastive alignment concepts
2. Componentization
- Model selection for discriminative tasks; prompt engineering and structured output design for generative tasks
- Fine-tuning approaches including LoRA and Reinforcement Learning from Human Feedback (RLHF)
- Model distillation, selection criteria, and inference serving tradeoffs
- Component-level failure modes and graceful degradation patterns
3. Composition
- Discriminative pipeline patterns: cascade detectors, multi-task architectures, and detection-and-tracking pipelines
- Retrieval-Augmented Generation (RAG) architectures, multi-agent orchestration, and routing strategies
- Hybrid classical and foundation model pipelines
- Error propagation analysis and cascading failure prevention
4. Integration
- Cognitive task analysis and workflow re-engineering frameworks
- Levels of automation and human-AI collaboration design
- Trust calibration, transparency requirements, and human factors considerations
- Modality-aware interaction patterns for operational environments
5. Assurance
- Testing strategies for composed AI systems in production
- Monitoring, observability, and performance tracking approaches
- Governance frameworks and safety considerations
- System-level validation and reliability assessment methods
Materials
Required:
- Laptop or Tablet
Provided:
- PPT deck of course materials
Session Details
CRN
25313CRN
24343Who Should Attend
This course is designed for software architects, engineering managers, product managers, system integrators, and technical leaders responsible for deploying AI capabilities in operational environments. It is ideal for professionals who need to bridge the gap between AI technical capabilities and practical implementation, including those overseeing AI programs, evaluating vendor solutions, or integrating foundation models into existing workflows.
What You Will Learn
- Classify and compare AI system types — classifiers, detectors, generators, trackers, rankers, and agents — against deployment requirements and operational constraints
- Adapt discriminative models (selection, transfer learning, calibration) and generative models (prompt engineering, fine-tuning, distillation) into reliable components
- Design composed AI systems: discriminative detection and tracking pipelines, RAG architectures, agentic systems, and hybrid cross-paradigm patterns
- Allocate decisions between human and AI using autonomy frameworks, and design interfaces that produce calibrated trust rather than overtrust
- Deploy testing, monitoring, and governance strategies to assure AI systems in production
- Apply interpretability, robustness, fairness, and safety engineering to discriminative, generative, and hybrid architectures
How You Will Benefit
- Identify which type of AI system is appropriate for a given operational requirement, and articulate what it will and will not do
- Evaluate whether an AI component is production-ready, based on its interface contract, failure characterization, and monitoring design — for both discriminative models (classifiers, detectors, rankers) and generative models (LLMs, RAG systems, agents)
- Make principled autonomy allocation decisions: what the machine should decide, what the human should decide, and what the interface between them needs to communicate
- Construct or evaluate an assurance argument for a deployed AI system — covering performance, interpretability, robustness, fairness, safety, and governance
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Grow Your Professional Network
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Taught by Experts in the Field
The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.
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