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

18 June 2020

DURATION

10 weeks, online
4-6 hours per week

Why enrol for the Imperial Machine Learning for Decision Making programme?

Imperial Machine Learning for Decision Making is an online programme brought to you by Executive Education at Imperial College Business School. This immersive and interactive programme will expand your understanding of machine learning (ML) and teach you the tools and techniques used for applying machine learning to business scenarios.

You will have the opportunity to apply your learning to real life business problems, using analytical techniques such as clustering, classification and regression. This programme will enable you to communicate credibly with those who have deep technical knowledge in the field, having a greater impact within your organisation. Throughout the programme, you will draw on expertise from Imperial College Business School faculty, industry experts, case studies and your peers.

The 10 weeks online programme combines live online teaching sessions and video lectures with interactive activities and assignments to enable high-impact learning. You will receive personal support throughout the programme from a dedicated Learning Team and finish the programme prepared to implement what you’ve learnt. On completion you will receive a verified Digital Certificate from Imperial College Business School Executive Education.

Who is this programme for?

This programme is designed for data and technology professionals across all functions as well as professionals and students interested in learning more about the field of machine learning.

Its content is applicable across industries from banking and financial services, consulting, education and energy to healthcare, IT products and services and retail.

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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.
Programme highlights
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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Modules

Prerequisite: This programme will require prior knowledge of statistics, probability, and linear algebra.

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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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Early applications encouraged.

Flexible payment options available. Click here to know more.