AI Agents Multi Agent Design Governance Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Foundations of Computing & Algorithms
Estimated time: 2 hours
- Building practical solutions through interactive labs
- Discussion of best practices and industry standards
- Hands-on exercises applying computing fundamentals
- Applying algorithms techniques in real-world contexts
Module 2: Neural Networks & Deep Learning
Estimated time: 3 hours
- Introduction to key concepts in neural networks
- Understanding deep learning fundamentals
- Discussion of best practices and industry standards
- Guided project work with instructor feedback
Module 3: AI System Design & Architecture
Estimated time: 3 hours
- Assessment through quiz and peer-reviewed assignment
- Discussion of best practices and industry standards
- Case study analysis with real-world examples
- Review of tools and frameworks used in AI systems
Module 4: Natural Language Processing
Estimated time: 4 hours
- Discussion of best practices and industry standards
- Review of NLP tools and frameworks
- Assessment via quiz and peer-reviewed assignment
Module 5: Computer Vision & Pattern Recognition
Estimated time: 2 hours
- Introduction to key concepts in computer vision
- Review of tools and frameworks in pattern recognition
- Case study analysis with real-world examples
- Hands-on exercises applying computer vision techniques
Module 6: Deployment & Production Systems
Estimated time: 4 hours
- Discussion of best practices and industry standards
- Guided project work with instructor feedback
- Assessment through quiz and peer-reviewed assignment
Prerequisites
- Prior knowledge of AI fundamentals
- Basic programming experience
- Familiarity with machine learning concepts
What You'll Be Able to Do After
- Design and implement multi-agent AI systems
- Evaluate AI models using performance metrics and benchmarks
- Apply prompt engineering techniques to large language models
- Build and deploy AI-powered applications
- Ensure governance, safety, and scalability in AI architectures