Course Code
aicityplanning
Duration
14 hours (usually 2 days including breaks)
Requirements
- An understanding of city planning
- A basic understanding of programming concepts
Overview
What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?
In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.
Audience
- City planners
- Architects
- Developers
- Transportation officials
Format of the Course
- Part lecture, part discussion, and a series of interactive exercises.
Note
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
- AI for city planning
Uses and Opportunities for City Service Providers
- Architecture, transportation, public safety, land use, environment, etc.
Applications for AI
- Computer Vision, Natural Language Procession (NLP), Voice Recognition, etc.
The Data Behind AI
- Data as the enabler of AI
- Gaining access to the data
The Computation behind AI
- Probability and Statistics as the Core
- How Algorithms Enable Intelligence
The Logic Behind AI
- Programming Language used in AI
- Needed skillsets
Teaching Machines How to Learn
- Understanding machine learning
- Applying machine learning libraries to develop intelligent systems
Advanced Approaches to Machine Learning
- Deep Learning
Case Study
- Predicting traffic bottlenecks with machine learning
The Tooling behind AI
- Different databases for different purposes
- Data processing engines
- Building the infrastructure on premise or in the cloud
Analyzing the Data
- Handling large volumes of data
- Aggregating data across agencies
- Data preparation, staging, analysis and reporting
- Data mining approaches
Case Study
- Collecting, filtering and analyzing demographic data by neighborhood
The Interplay of AI and IoT
- Cameras, sensors, actuators, etc.
- Assessing the city's network infrastructure
Autonomous Decision Making and Execution
- Using rules engines and expert systems to make decisions
- Programming machines to take actions on their own
Case Study
- Responding to emergencies based on real-time data
Automating Human Processes
- The interplay of humans and machine
- Optimizing processes in municipal departments
Bringing it All Together
- The low-hanging fruit for city planners
- Constructing a city wide digital platform
Planning and Communicating an AI Strategy
- Needs assessment and return on investment
- Bringing together city leaders, agencies, businesses and universities
Summary and Conclusion
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