Big data in the public sector

Crawford School of Public Policy | Executive course
Economics, Data Analysis and Decision Making

Summary

The possibility of using big data in combination with machine learning algorithms creates a range of challenges and opportunities for policymakers. Understanding these is not only essential for the responsible application of machine learning tools to administrative records but also for the design of appropriate data protection laws and - where necessary - the informed regulation of private sector activity.

This course will develop your skills to understand the intuition behind relevant machine learning tools, provide examples for how to apply these tools using the software package Python, and explain how to interpret and compare competing machine learning systems. The course will conclude with a discussion of the risks and opportunities associated with the application of machine learning algorithms.

Course date: 
9.30am–4.30pm 18 September 2019
Venue: 
#132 Crawford Building, Lennox Crossing, ANU
Cost: 

$1,195

Course overview

The possibility of using big data in combination with machine learning algorithms creates a range of challenges and opportunities for policymakers. Understanding these challenges and opportunities is not only essential for the responsible application of machine learning tools to administrative records but also for the design of appropriate data protection laws and - where necessary - the informed regulation of private sector activity.

The course will cover four main topics:

  1. Introduction and overview
  2. Applications and examples
  3. Model competition
  4. Guided group discussion of challenges and opportunities

The course will provide an overview of relevant machine learning tools, explain the intuition behind these tools, illustrate their application using the publicly available software package Python, and explain how to interpret and compare competing machine learning systems (such as Lasso, Ridge, Elastic Net, Trees, Random Forests, Boosting, Stacking). The course will conclude with a guided group discussion of the risks and opportunities associated with the application of machine learning algorithms.

The course will provide participants with the knowledge they require to understand the intuition behind relevant machine learning algorithms. Students will learn how to get started using the publicly available software package Python to analyse big data. The course will combine intuitive explanations with practical examples. The course is suitable for beginners.

Course presenter(s)

Associate Professor Mathias Sinning

Mathias Sinning is an Associate Professor at the ANU Crawford School of Public Policy. He has previously held academic appointments at the ANU and the University of Queensland and has been a Visiting Fellow at Princeton University. Mathias is interested in the empirical analysis of issues related to labor economics, public economics and policy evaluation. He has published empirical research in a wide range of international peer-reviewed journals, including Economics of Education Review, Economic Inquiry, Health Economics, Industrial and Labor Relations Review, Journal of Banking and Finance, Journal of Economic Behavior and Organization, Journal of Population Economics, Regional Science and Urban Economics and Review of Income and Wealth. Mathias has extensive experience in applying experimental and non-experimental methods to evaluate policies and has worked with government departments on a range of projects, including on issues related to behavioral economics, labor economics and public economics.

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