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
27 November 2023 – 19 January 2024

I. About the Course
This course introduces machine learning as a tool for using either traditional (surveys,micro data, …) or non-traditional 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 and their links with traditional statistical methods. It aims
at providing an overview of the current methods and applications of Machine Learning,
through simplified 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 modules.
Each module is composed of several mandatory pedagogical activities, following a logicalstructure. Activities include videos, interactive videos, interactive web-based apps, chats,
live lectures and 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. These notions will be usedthroughout the course.
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 areexpected to interact during live sessions and through forums embedded in each module.
Instructions, conversations, pedagogical resources and webinars are in English.
Machine Learning for Official Statistics and SDG Indicators 2023

Page 1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 algorithmic
skills. 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
Recognize the drawbacks and biases of ML
Identify Ethical issues when using Machine Learning

IV. Course Design, Workload and Content
The course is divided in 6 (+1) modules and will last for 8 weeks. The elements (slides,
code and references) used for each activity will be made available for download in theLMS in the form of pdf or html documents. Optional documents include R-markdown
documents with reproducible code. Python users will find some code and functions
similar to ones used in R in the Markdown documents.
The workload of this course is of 3h/week. It is thus important for participants toorganize themselves and allocate a sufficient amount of learning time during the course.

Machine Learning for Official Statistics and SDG Indicators 2023

Page 2ModuleCoverage
M0: Statistical Learning: “You’ve seen this• Linear and non-linear regression
before”••••

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 friendsSelection of regressors
Penalization Methods
How to choose the best model?

•••••

Decisions Trees
Visualizing a decision tree
Bagging and Boosting
Random ForestRandom Forest and Imputation•••

Support Vector Machines
Unsupervised Learning (k-Means)
Introduction to Neural Networks

M6: “Real-World Machine Learning”:
Part 1: Estimating Poverty From Satellite Images(Asian Development Bank)•Part 2: Ethical Considerations in Machine
Learning•

Using satellite imagery to estimate poverty- Training a Neural Network- From images to data- Predicting poverty and visualization
Ethical considerations in the use of Machine
Learning- Which Ethical and Legal Constrains
for Machine Learning projects?

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: Advanced
Methods

Machine Learning for Official Statistics and SDG Indicators 2023

Page 3

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 thevarious 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
- Participate in all weekly webinars- Complete all the weekly assessments (tests) in due time
Participants must complete the end-of-course evaluation to receive a certificate.

Machine Learning for Official Statistics and SDG Indicators 2023

Page 4