Data Science for Public Policy

Huynh Nhat Nam, Vladimir Yapit Mariano & Vo Tuan Kiet
Date: 26/10/2022 23:29; File size: 119,150 bytes
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The first part of the course will introduce the fundamental principles and concepts underlyingcommon algorithms in machine learning and their applications in business and policy. Morespecifically, students will first be introduced to the concepts of bias-variance tradeoff andoverfitting, which are arguably the bedrock of developing effective predictive analytic models.

Techniques to help identify overfitting (e.g. cross-validation) and eliminate overfitting (e.g.regularisation) of predictive models will be presented. While students may have been familiarwith parametric approaches to predictive analytics (e.g. regression models introduced in ‘Quantitative Methods’ courses), this course aims to introduce students to non-parametricapproaches (with a focus on classification problems), such as K-nearest neighbours, treebasedmethods, and support vector machines. They will also be introduced to the principlesand application of popular unsupervised methods, such as hierarchical and k-means clusteringalgorithms.

The second part of the course will introduce students to the use of off-the-shelf artificialintelligence libraries for image processing, with a focus (and hands-on exercises) on their usefor satellite image processing, facial recognition and vehicle counting.

The courses will consist of lectures and computing exercises which are designed to helpstudents practice and thus understand better the theoretical concepts presented in thelectures. The lectures are not intended to be mathematical intensive. Mathematical details willbe provided just enough to help students understand the data science concepts andassociated techniques. The programming language Python will be used to demonstrate theseconcepts and techniques. Students will be required to write codes in Python for exercises,assignments and the final exam. While a brief introduction to Python for data manipulation willbe provided, it is recommended that students have prior knowledge of programming, eitherwith Python or another language.

The course will be taught in English (without interpretation).


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