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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
Requirements
- An understanding of city planning
- A basic understanding of programming concepts
14 Hours
