Find the presentation and outcome slides of this event attached.

Access the source code to produce the figures in the slides and illustrates how the concepts are implemented.

Watch the recording of the event.

Opening Remarks

Hyoung Gun Wang, Senior Economist & Smart Cities KSB Lead, WBG

Presenters

Jon Kastelan, Machine Learning Specialist

Nick Jones, DRM Specialist, WBG

Overview: Machine learning (ML) is a set of methods to recognize patterns and make predictions from large datasets. City governments are increasingly using ML methods to help serve their citizens better. This workshop provides staff with an understanding of the nuts-and-bolts of ML in a smart city context. Focused on a simple but powerful family of models – supervised classification – we will walk through the key steps to build and deploy a predictive tool for city authorities. The workshop will familiarize participants with key ML concepts including training and test data, identifying important features for prediction, and model evaluation metrics. Having surveyed recent applied examples, from prioritizing building inspections to targeting energy efficiency upgrades, participants will be invited to brainstorm potential applications in a city of their choice.  

Audience: The workshop is open to all Bank staff and consultants with no technical background needed.

Objectives: (i) understand the key steps to build and deploy a machine learning model for city authorities; (ii) identify untapped datasets and use cases where Machine Learning can help your clients.

Agenda:

Find the presentation and outcome slides of this event are attached.

Accesse the source code to produce the figures in the slides and illustrates how the concepts are implemented.

Watch the recording of the event.

Opening Remarks

Hyoung Gun Wang, Senior Economist & Smart Cities KSB Lead, WBG

Presenters

Jon Kastelan, Machine Learning Specialist

Nick Jones, DRM Specialist, WBG

Overview: Machine learning (ML) is a set of methods to recognize patterns and make predictions from large datasets. City governments are increasingly using ML methods to help serve their citizens better. This workshop provides staff with an understanding of the nuts-and-bolts of ML in a smart city context. Focused on a simple but powerful family of models – supervised classification – we will walk through the key steps to build and deploy a predictive tool for city authorities. The workshop will familiarize participants with key ML concepts including training and test data, identifying important features for prediction, and model evaluation metrics. Having surveyed recent applied examples, from prioritizing building inspections to targeting energy efficiency upgrades, participants will be invited to brainstorm potential applications in a city of their choice.  

Audience: The workshop is open to all Bank staff and consultants with no technical background needed.

Objectives: (i) understand the key steps to build and deploy a machine learning model for city authorities; (ii) identify untapped datasets and use cases where Machine Learning can help your clients.

Agenda:

You could find the presentation and outcome slides of this event attached.

Access the source code to produce the figures in the slides and illustrates how the concepts are implemented here.

Watch the recording of the event here

Opening Remarks

Hyoung Gun Wang, Senior Economist & Smart Cities KSB Lead, WBG

Presenters

Jon Kastelan, Machine Learning Specialist

Nick Jones, DRM Specialist, WBG

Overview: Machine learning (ML) is a set of methods to recognize patterns and make predictions from large datasets. City governments are increasingly using ML methods to help serve their citizens better. This workshop provides staff with an understanding of the nuts-and-bolts of ML in a smart city context. Focused on a simple but powerful family of models – supervised classification – we will walk through the key steps to build and deploy a predictive tool for city authorities. The workshop will familiarize participants with key ML concepts including training and test data, identifying important features for prediction, and model evaluation metrics. Having surveyed recent applied examples, from prioritizing building inspections to targeting energy efficiency upgrades, participants will be invited to brainstorm potential applications in a city of their choice.  

Audience: The workshop is open to all Bank staff and consultants with no technical background needed.

Objectives: (i) understand the key steps to build and deploy a machine learning model for city authorities; (ii) identify untapped datasets and use cases where Machine Learning can help your clients.

Agenda:

A

WATCH THE RECORDING HERE.

Opening Remarks

Hyoung Gun Wang, Senior Economist & Smart Cities KSB Lead, WBG

Presenters

Jon Kastelan, Machine Learning Specialist

Nick Jones, DRM Specialist, WBG

Overview: Machine learning (ML) is a set of methods to recognize patterns and make predictions from large datasets. City governments are increasingly using ML methods to help serve their citizens better. This workshop provides staff with an understanding of the nuts-and-bolts of ML in a smart city context. Focused on a simple but powerful family of models – supervised classification – we will walk through the key steps to build and deploy a predictive tool for city authorities. The workshop will familiarize participants with key ML concepts including training and test data, identifying important features for prediction, and model evaluation metrics. Having surveyed recent applied examples, from prioritizing building inspections to targeting energy efficiency upgrades, participants will be invited to brainstorm potential applications in a city of their choice.  

Audience: The workshop is open to all Bank staff and consultants with no technical background needed.

Objectives: (i) understand the key steps to build and deploy a machine learning model for city authorities; (ii) identify untapped datasets and use cases where Machine Learning can help your clients.

Agenda:

Hyoung Gun Wang's presentation is attached. Watch the recording here.

For any questions please contact Jung Choi

Hyoung Gun Wang's presentation is attached. Watch the recording here.

For any questions please contact Jung Choi

Hyoung Gun Wang's presentation is attached. Watch the recording here.

For any questions please contact Jung Choi

Play recording

For any questions please contact Jung Choi

A

WATCH THE RECORDING HERE.

Opening Remarks

Hyoung Gun Wang, Senior Economist & Smart Cities KSB Lead, WBG

Presenters

Jon Kastelan, Machine Learning Specialist

Nick Jones, DRM Specialist, WBG

Overview: Machine learning (ML) is a set of methods to recognize patterns and make predictions from large datasets. City governments are increasingly using ML methods to help serve their citizens better. This workshop provides staff with an understanding of the nuts-and-bolts of ML in a smart city context. Focused on a simple but powerful family of models – supervised classification – we will walk through the key steps to build and deploy a predictive tool for city authorities. The workshop will familiarize participants with key ML concepts including training and test data, identifying important features for prediction, and model evaluation metrics. Having surveyed recent applied examples, from prioritizing building inspections to targeting energy efficiency upgrades, participants will be invited to brainstorm potential applications in a city of their choice.  

Audience: The workshop is open to all Bank staff and consultants with no technical background needed.

Objectives: (i) understand the key steps to build and deploy a machine learning model for city authorities; (ii) identify untapped datasets and use cases where Machine Learning can help your clients.

Agenda:

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For any questions please contact Jung Choi