UNITED NATIONSECONOMIC AND SOCIAL COMMISSION FOR ASIA AND THE PACIFIC (ESCAP)STATISTICAL INSTITUTE FOR ASIA AND THE PACIFIC (SIAP)
In partnership withTHE ASIAN DEVELOPMENT BANK (ADB)

e-Learning Course
Machine Learning for Official Statistics
and SDG Indicators
8 November – 17 December 2021

I. About the Course
This course introduces machine learning as a tool for using either traditional or nontraditional data sources (big data) to produce high quality predictions for official statistics
or Sustainable Development Goals (SDGs) indicators.
The course provides an opportunity for participants to explore and comprehend thetechniques of machine learning in comparison with more traditional statistical methods. It
aims at providing an overview of the current methods and applications of Machine
Learning, through theoretical concepts, pedagogical case studies and interactive resources.
The course is not based, nor does it require, a particular software. However, reproducible
examples on either simulated or real data are provided using the R/RStudio environment.
Some Python procedures and packages are also provided.
The e-course has been developed as an interactive training composed of 6 + 1 module.
Each module is composed of several mandatory pedagogical activities, following a logicalstructure. Activities include videos, interactive videos, interactive web-based apps, chats,
live lectures, webinars, document reading, exercises, polls and quizzes. A preliminary
module, M0, serves as a reminder and proposes a summary of statistical notions,terminology and basic concepts used by data analysts and that will be used throughout thecourse.
The course is hosted on the SIAP’s Learning Management System (LMS) which contains
a forum for general questions and interactions with the SIAP’s lecturers and e-learning
platform administrators. Mandatory weekly webinars, in the form of presentations or
Q&A sessions will be proposed using the Microsoft Teams platform. The participants are1

expected to interact during live sessions and through forums embedded in each module.
Instructions, conversations and webinars are in English.

II. Target Audience
The course is designed for personnel working in the field of statistics, whose main
responsibilities include data analysis of SDG indicators and related statistics with a specifictarget on data scientists from NSOs with an experience in both statistical modelling(regression analysis, prediction, classification, ...) and with programming or algorithmicskills. The participants must have a good practice in the manipulation of data as well as agood understanding of statistical methods. They must have some experience inprogramming in any scientific or statistical language (R, Python, Stata, SAS, Matlab, ...).
Although no programming will be required to follow and succeed in the course, thepedagogical materials include R code, in the form of reproducible markdown notebooks,
as well as some Python resources and code.

III. Learning Objectives
At the end of the course, the learners should be able to:•••••••••••••••

Evaluate the potential use of ML for official statistics and SDGs
Compare classical statistical and ML methods
Describe the features of ML techniques
Describe the main steps of a ML project
Differentiate Supervised vs Unsupervised ML problems
Manipulate classification algorithms
Perform Regressors selection using ML techniques
Produce predictions using ML algorithms
Evaluate the quality of ML predictions
Apply data visualization techniques to assess the quality of prediction
Select the right criteria/ visualization for determining hyperparameters
Identify the limitations of ML for official Statistics and SDGs monitoring
Interpret ML outcomes and predictions
Apply ML techniques using either R or Python on structured data sets
Recognize the drawbacks and biases of ML

IV. Course Design and Content
The course is divided in 6 (+1) modules. It will last for 6 weeks and will requireapproximately 3h of work each. The elements (slides, code and references) used for each2activity will be made available for download in the LMS in the form of pdf or html
documents.

ModuleCoverage
M0: Statistical Learning: “You’ve seen this• Linear and non-linear regression
before”

M1: Machine Learning for Official Statistics andthe SDGs

M2: Machine Learning in Practice: Classification

M3: Machine Learning in practice: Regression

M4: Machine Learning in Practice: Decision Trees

M5: Machine Learning in Practice: AdvancedMethods

M6: Using Machine Learning in the real world: Acase study from Asian Development Bank3••••

Supervised vs unsupervised learning
k-Nearest Neighbours
Statistical Learning vs Machine Learning
Cross validation••••

Introduction to the course
Using Machine Leaning for Official Statistics
Tools for applied Machine Learning
Limitations of Machine Learning predictions

••
•
•
•
•

How classification works
Supervised vs unsupervised classification
Examples of classifiers
Measures of fit
Logit as a classifier
How to choose the best model?

••••

Linear Regression and all his friends
Selection of regressors
Penalization Methods
How to choose the best model?

•••••Decisions Trees
Visualizing a decision tree
Bagging and Boosting
Random Forest
Random Forest and Imputation•••Support Vector Machines
Unsupervised Learning (k-Means)
Introduction to Neural Networks•

Using satellite imagery to estimate poverty- Training a Neural Network- From images to data

-Predicting poverty and visualization

V. Evaluation
The evaluation will be based on the learner’s ability to apply the methods and training
materials described during the course, as well as on their capacity to recall the main
outcome of each module. The final grade will be based on the result to mandatory
assessments exercises, in the form of Multiple Choice Questions (MCQ)-like tests tocomplete at the end of each module.
.
An individual data-based project will be proposed as an optional activity and will provide
some bonus points if completed before the end of the course. Participation to the various
chats and Q&A will also provide bonus points.
To succeed in the course participants must have at least a final score of 70/100, and comply
with the following conditions:- Follow all of the mandatory activities in due time
- Complete all the weekly assessments (tests) in due time
Participants must complete the end-of-course evaluation to receive a certificate.

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