What you'll learn
- Explore the current state of Artificial Intelligence and Machine Learning (ML), with particular emphasis on their applications in the fields of Architecture, Landscape, Urbanism and Real Estate, especially in Proptech.
- Learn the five rules about which types of problems Artificial Intelligence and Machine Learning are the right answer for tackling.
- Understand the importance of data acquisition and parsing for machine learning training, as well as identify potential issues of bias and its ethical implications.
- Acquire the skills to manage a team in a successful machine learning project, without needing the expertise to understand the details of its technical implementation.
- Build your own organizational guide on the steps you can take immediately to ensure successful future implementations of Artificial Intelligence and Machine Learning in your projects.
The main focus of the program will be to give you a high-level overview of what AI & ML are, and what types of problems they are particularly suited to solve. We will present the foundational topic of data, including types, acquisition, parsing and their relation to the training of neural networks, as well as more advanced themes such as biases and ethics. This three-day program will be preceded by short readings, and consist of lectures, hands-on conceptual exercises and group discussions focused on current practical applications of AI & ML in the built environment. Past iterations have looked at the applications of machine learning on property valuation, floorplan generation, recommendation engines, and listing process automation, as used by the worlds most prominent proptech companies, such as Airbnb, Zillow, and Redfin. Given the rate of iteration of AI & ML, each session looks at the most up-to-date examples shaping the industry.
By the end of the program, you will understand what applications of AI & ML offer your practice a potential competitive advantage, and what procedures need to be put in place to ensure successful AI & ML project implementation. Finally, you will gain the background skills necessary to lead a technical team in a machine learning project of your own.
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