THE LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE

MACHINE LEARNING:
PRACTICAL APPLICATIONS

Upgrade your data skills and develop a technical understanding of the business applications of machine learning.

Duration

8 weeks, excluding
orientation

Effort

7–10 hours per week,
entirely online

Learning Format

Weekly modules,
flexible learning

ON COMPLETION OF THIS COURSE, YOU’LL WALK AWAY WITH:

1

An in-depth understanding of various machine learning techniques, including regression, ensemble learning, and tree-based methods, among others.

2

The ability to code in R and apply machine learning techniques to various types of data.

3

Exposure to the latest frontiers of machine learning, such as neural networks and how these can be applied in business.

4

A certificate of competence from LSE, a world-leading social science university.

COURSE CURRICULUM

Over the duration of this online certificate course, you’ll work your way through the following modules:

MODULE 1
Learning from data

Discover the appropriate use and processing of data for optimising machine learning applications.

MODULE 2
Principles of machine learning

Understand the fundamental principles of machine learning and the flow of the machine learning pipeline.

MODULE 3
Regression

Explore regression as a supervised machine learning technique to predict a continuous variable (response or target) from a set of other variables (features or predictors).

MODULE 4
Variable selection and shrinkage methods

Discover how variable selection and shrinkage methods are used to improve the efficiency of a regression model when applied to complex data sets.

MODULE 5
Classification

Explore classification as a supervised machine learning technique to predict binary (or discrete) response variables from a set of features.

MODULE 6
Tree-based methods and ensemble learning

Discover how tree-based methods and ensemble learning methods are applied to improve the accuracy of a prediction.

MODULE 7
Introduction to neural networks

Understand what neural networks are, its most successful applications, and how it can be used within a business context.

MODULE 8
Unsupervised learning

Explore the process of unsupervised machine learning techniques of clustering and dimension reduction as a means of learning from unlabelled data.

PREREQUISITES

This course is technical in nature. It makes use of coding in R and covers the application of machine learning in business. Some algebraic and calculus knowledge is strongly advised, but is not required. Tertiary level statistics and knowledge of a functional or object oriented language is advantageous. HTML is not considered a programming language in this context. No specific software is required for this online certificate course.

YOUR COURSE CONVENORS

Dr Kostas Kalogeropoulos

Associate Professor of Statistics, London School of Economics and Political Science

With a focus on developing and applying advanced computational and machine learning methods, Kostas’s research methodology has mostly targeted continuous time probability models based on stochastic differential equations driven by standard or fractional Brownian motion. Other areas include factor analysis and sequential learning. His research finds applications in financial and econometric time series, as well as biomedical problems such as stochastic epidemic models and analysis of growth curves.

Prior to joining the statistics department at LSE, he was a postdoctoral researcher at the University of Cambridge in the Signal Processing Laboratory of the engineering department. He completed his PhD (2007) in the statistics department of the Athens University of Economics and Business while spending some time at the University of Lancaster.

Dr Xinghao Qiao

Assistant Professor of Statistics, London School of Economics and Political Science

Xinghao’s research is extensive. He is focused on functional and longitudinal data analysis as well as high dimensional statistical inference such as covariance and precision matrix estimation, and variable selection. He’s further interested in time series analysis such as functional time series and high dimensional time series. Xinghao also analyses statistical machine learning with applications in business, neuroimaging analysis and environmental sciences.

Prior to joining LSE as an assistant professor in statistics, Xinghao earned his PhD in business statistics from Marshall School of Business at the University of Southern California, his MS in statistics at the University of Chicago, and BS in mathematics and physics at Tsinghua University.

Dr Yining Chen

Assistant Professor of Statistics, London School of Economics and Political Science

Yining's current research focuses on developing new methods for statistical problems, such as change-point detection and nonparametric estimation. He is also interested in understanding the computational aspects of statistical methods. He completed his PhD (2014) in statistics at the University of Cambridge.

AN ONLINE EDUCATION THAT SETS YOU APART

This LSE online certificate course is delivered in collaboration with online education provider GetSmarter. Join a growing community of global professionals who've already had the opportunity to:

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Gain verifiable and relevant competencies and earn invaluable recognition from a world-leading social science university, entirely online and in your own time

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Enjoy a personalised, people-mediated online learning experience created to make you feel supported at every step

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Experience a flexible but structured approach to online education as you plan your learning around your life to meet weekly milestones

VIEW COURSE PROSPECTUS

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