Artificial Intelligence (AI) for City Planning Training Course
What will the cities of tomorrow look like? How can Artificial Intelligence (AI) enhance urban planning processes? How can AI contribute to making urban areas more efficient, habitable, secure, and eco-friendly?
In this instructor-led live training (available onsite or remotely), we explore the various technologies that comprise AI, along with the skills and mindset needed to apply them effectively to urban planning. We also discuss tools and methods for collecting and organizing relevant data for AI applications, including data mining techniques.
Target Audience
- Urban planners
- Architects
- Developers
- Transportation officials
Course Format
- A blend of lectures, discussions, and a series of interactive exercises.
Note
- To request customized training for this course, please contact us to arrange.
Course Outline
Introduction
- AI in urban planning
Opportunities for City Service Providers
- Architecture, transportation, public safety, land use, environment, and more.
AI Applications
- Computer Vision, Natural Language Processing (NLP), Voice Recognition, and more.
The Data Foundation of AI
- Data as the key enabler of AI
- Accessing data resources
The Computational Engine Behind AI
- Probability and Statistics as the Foundation
- How Algorithms Drive Intelligence
The Logic of AI
- Programming languages used in AI
- Required skill sets
Teaching Machines to Learn
- Understanding machine learning concepts
- Applying machine learning libraries to build intelligent systems
Advanced Machine Learning Techniques
- Deep Learning
Case Study
- Predicting traffic congestion using machine learning
The Tooling Behind AI
- Databases tailored for specific purposes
- Data processing engines
- Building infrastructure either on-premise or in the cloud
Data Analysis
- Managing large volumes of data
- Aggregating data across departments
- Data preparation, staging, analysis, and reporting
- Data mining methodologies
Case Study
- Collecting, filtering, and analyzing demographic data by neighborhood
The Interaction Between AI and IoT
- Cameras, sensors, actuators, and more.
- Evaluating the city's network infrastructure
Autonomous Decision-Making and Execution
- Using rules engines and expert systems for decision-making
- Programming machines to act independently
Case Study
- Responding to emergencies using real-time data
Automating Human Processes
- The relationship between humans and machines
- Optimizing processes in municipal departments
Synthesizing Everything
- Quick wins for city planners
- Building a city-wide digital platform
Planning and Communicating an AI Strategy
- Needs assessment and return on investment
- Collaborating with city leaders, agencies, businesses, and universities
Summary and Conclusion
Requirements
- A solid understanding of urban planning principles
- A foundational knowledge of programming concepts
Open Training Courses require 5+ participants.
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