COVID-19 STATEMENT: While this virus is impacting everyone differently, this online programme is continuing as planned.
Please consider joining our global online classroom for an enriching and interactive experience to further your career.

STARTS ON

27 August 2020

DURATION

10 weeks, online
4-6 hours per week

Programme highlights

Gain a practical understanding of the tools and techniques used in machine learning applications for business. By the end of this programme, you will be able to:

  • Characterise the fundamental machine learning problem and outline the ten steps in a typical machine learning project.
  • Explain why we may not be able to draw meaningful conclusions from experience and calculate the probability of a function providing the correct outcome.
  • Outline the steps to selecting a machine learning model, select the best fit based on the training set and the validation set and predict a model’s performance.
  • Differentiate between ranking and prediction problems. Use performance measures to evaluate regression problems, a confusion matrix to evaluate classification problems and lift charts to evaluate ranking problems.
  • Use oversampling to improve the misclassification rate on interesting cases and the K-fold cross-validation algorithm to overcome shortcomings of the training set-validation set approach.
  • Understand real-life applications of k-nearest neighbours and use k-nearest neighbours methods for classification and regression.
  • Apply the Naïve Bayes Theorem to calculate conditional probabilities and explore its real-life applications.
  • Utilise classification and regression trees to solve real-life problems.
  • Define proximity for clustering methods and understand the steps involved in hierarchical and k-means clustering and their related applications.
AdWeek Survey

75%

of Netflix users select films recommended to them by the company’s ML algorithms

SOURCE: FORBES, JAN 2020
AdWeek Survey

$20.8B

is the projected global ML market value by 2024

SOURCE: ZION MARKET RESEARCH, NOV 2018
Pinterest

$28.5B

Investment in ML application in Q1 2019

SOURCE: STATISTA, MAY 2019
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Who is this programme for?

This programme is designed for experienced managers and executives working in technology, including:

The programme is relevant across industries, including: IT Products & Services, Banking & Financial Services, Healthcare, Consulting, Education, FMCG, Retail and Telecommunications.

No prior programming experience required.

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Modules

Prerequisite: This programme will require prior knowledge of statistics, probability and linear algebra.
Note: For those wanting to develop deeper skills with analytics, academic credit from this programme can be applied to the Imperial MSc in Business Analytics in the future.

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Faculty

Prof Wolfram Wiesemann
Professor Wolfram Wiesemann
Professor of Analytics and Operations,
Imperial College Business School

Wolfram Wiesemann is Professor of Analytics and Operations at Imperial College Business School, London, where he also serves as the Academic Director of the MSc Business Analytics programme as well as a Fellow of the KPMG Centre for Advanced Business Analytics...
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Certificate

Imperial Machine Learning for Decision Making programme Certificate

Certificate

Upon completion of the programme, participants will be awarded a verified Digital Certificate by Imperial College Business School Executive Education.

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Future learning

For those who want to progress their skills to the next level, an academic credit from this programme can be applied to the Imperial MSc in Business Analytics in the future.

The MSc programme enables graduates to understand the challenge of managing large data sets and to provide them with a skill set to meet this challenge. The programme combines academic rigour and practical relevance. To learn more, visit the programme website.

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Early applications encouraged.

Flexible payment options available. Click here to know more.