Imperial Machine Learning for Decision Making

No prior programming knowledge required

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Course Dates

STARTS ON

TBD

Course Duration

DURATION

10 weeks, online
4-6 hours per week

Course Fee
Course Fee

For Your Team

Enrol your team and learn with your peers

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Team-Based Learning Options

  • Enrol as a team or group and learn with your peers

  • Receive support and services

  • Inquire about special team/group pricing

  • ENROL YOUR TEAM

    Emeritus works with leading companies to close critical skills gaps


    Our partners include

    JP Morgan
    Amazon
    AB in Bev

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.
Decorative image relating to Netflix users ML algorithm

75%

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

SOURCE: FORBES, JAN 2020
Decorative image relating to  global ML market value stats

$20.8B

is the projected global ML market value by 2024

SOURCE: ZION MARKET RESEARCH, NOV 2018
Decorative image relating to Investment in ML application stats in Q1 2019

$28.5B

Investment in ML application in Q1 2019

SOURCE: STATISTA, MAY 2019

Who is this programme for?

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

  • Mid to senior-level technical managers looking to build a better understanding of machine learning tools and techniques.
  • Technology management executives seeking to build machine learning capabilities in their function or organisation.
  • Consultants aiming to develop their knowledge of machine learning to offer better solutions to their clients.

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.

Modules

Module 1:

Introduction to Machine Learning

What is machine learning, the machine learning process, the machine learning landscape, machine learning in the real world.

Module 2:

The Fundamental Limits of Machine Learning

Is learning feasible at all, interpreting the bound, a probabilistic setting, when is machine learning feasible.

Module 3:

Evaluating Predictive Performance (I)

Which fit is "right", test set, validation set, the "training set - validation set - test set" approach.

Module 4:

Evaluating Predictive Performance (II)

Performance measures for regression, lift charts for classification problems, problems (use confusion matrix), lift charts for regression problems.

Module 5:

Evaluating Predictive Performance (III)

Oversampling, k-fold cross-validation.

Module 6:

K-Nearest Neighbours

K-nearest neighbours for classification, binary and categorical predictors, k-nearest neighbours for regression, distance functions, how should we choose k.

Module 7:

Naïve Bayes

Motivation, exact Bayes classifiers, the Laplace Estimator, Bayes' Theorem, naïve Bayes classifiers.

Module 8:

Classification and Regression Trees

Classification trees, choosing the best split: Part 2, regression trees, random forests and boosting algorithms, choosing the best split: Part 1, pruning a classification tree, bagging.

Module 9:

Cluster Analysis

Motivation, hierarchical clustering is myopic, practical concerns of a cluster, hierarchical clustering, k-means clustering, analysis.

Module 10:

Final Assignment

Module 1:

Introduction to Machine Learning

What is machine learning, the machine learning process, the machine learning landscape, machine learning in the real world.

Module 6:

K-Nearest Neighbours

K-nearest neighbours for classification, binary and categorical predictors, k-nearest neighbours for regression, distance functions, how should we choose k.

Module 2:

The Fundamental Limits of Machine Learning

Is learning feasible at all, interpreting the bound, a probabilistic setting, when is machine learning feasible.

Module 7:

Naïve Bayes

Motivation, exact Bayes classifiers, the Laplace Estimator, Bayes' Theorem, naïve Bayes classifiers.

Module 3:

Evaluating Predictive Performance (I)

Which fit is "right", test set, validation set, the "training set - validation set - test set" approach.

Module 8:

Classification and Regression Trees

Classification trees, choosing the best split: Part 2, regression trees, random forests and boosting algorithms, choosing the best split: Part 1, pruning a classification tree, bagging.

Module 4:

Evaluating Predictive Performance (II)

Performance measures for regression, lift charts for classification problems, problems (use confusion matrix), lift charts for regression problems.

Module 9:

Cluster Analysis

Motivation, hierarchical clustering is myopic, practical concerns of a cluster, hierarchical clustering, k-means clustering, analysis.

Module 5:

Evaluating Predictive Performance (III)

Oversampling, k-fold cross-validation.

Module 10:

Final Assignment

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

Faculty Member Professor 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... More info

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

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

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.

Please note that this programme contributes to earning Associate Alumni status. Visit the Associate Alumni page to find out more.

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Related Programmes

Imperial College Executive Education also provides the Live Virtual AI and Machine Learning in Financial Services programme. This programme builds a strong foundation in AI, big data, and machine learning and helps you explore what innovation will look like within financial services. Digital Transformation: 5 Game-Changing Technologies for Business.

LEARN MORE

FAQs

  • How do I know if this programme is right for me?

    After reviewing the information on the programme landing page, we recommend you submit the short form above to gain access to the programme brochure, which includes more in-depth information. If you still have questions about whether this programme is a good fit for you, please email learner.success@emeritus.org, and a dedicated programme advisor will follow up with you very shortly.


    Are there any prerequisites for this programme?

    Some programmes do have prerequisites, particularly the more technical ones. This information will be noted on the programme landing page, as well as in the programme brochure. If you are uncertain about programme prerequisites and your capabilities, please email us at learner.success@emeritus.org for assistance.


    Note that, unless otherwise stated on the programme web page, all programmes are taught in English, and proficiency in English is required.


    What is the typical class profile?

    More than 50 per cent of our participants are from outside the United States. Class profiles vary from one cohort to the next, but generally, our online certificates draw a highly diverse audience in terms of professional experience, industry and geography—leading to a very rich peer learning and networking experience.


    What other dates will this programme be offered in the future?

    Check back at this programme web page or email us at learner.success@emeritus.org to inquire if future programme dates or the timeline for future offerings have been confirmed yet.

  • How much time is required each week?

    Each programme includes an estimated learner effort per week. This is referenced at the top of the programme landing page under the Duration section, as well as in the programme brochure, which you can obtain by submitting the short form at the top of this web page.



    How will my time be spent?

    We have designed this programme to fit into your current working life as efficiently as possible. Time will be spent among a variety of activities, including:



    • Engaging with recorded video lectures from faculty
    • Attending webinars and office hours, as per the specific programme schedule
    • Reading or engaging with examples of core topics
    • Completing knowledge checks/quizzes and required activities
    • Engaging in moderated discussion groups with your peers
    • Completing your final project, if required

    The programme is designed to be highly interactive while also allowing time for self-reflection and to demonstrate an understanding of the core topics through various active learning exercises. Please contact us at learner.success@emeritus.org if you need further clarification on programme activities.



    What is it like to learn online with the learning collaborator, Emeritus?

    More than 250,000 professionals globally, across 80 countries, have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 per cent of the respondents of a recent survey across all our programmes said that their learning outcomes were met or exceeded.

    A dedicated programme support team is available 24/5 (Monday to Friday) to answer questions about the learning platform, technical issues or anything else that may affect your learning experience.


    How do I interact with other programme participants?

    Peer learning adds substantially to the overall learning experience and is an important part of the programme. You can connect and communicate with other participants through our learning platform.

  • What are the requirements to earn the certificate?

    Each programme includes an estimated learner effort per week, so you can gauge what will be required before you enrol. This is referenced at the top of the programme landing page under the Duration section, as well as in the programme brochure, which you can obtain by submitting the short form at the top of this web page. All programmes are designed to fit into your working life.

    This programme is scored as a pass or no-pass. Participants must complete the required activities to pass and obtain the certificate of completion. Some programmes include a final project submission or other assignments to obtain passing status. This information will be noted in the programme brochure. Please contact us at learner.success@emeritus.org if you need further clarification on any specific programme requirements.


    What type of certificate will I receive?

    Upon successful completion of the programme, you will receive a smart digital certificate. The smart digital certificate can be shared with friends, family, schools or potential employers. You can use it on your cover letter or resume and/or display it on your LinkedIn profile. The digital certificate will be sent approximately two weeks after the programme, once grading is complete.


    Can I get the hard copy of the certificate?

    No, only verified digital certificates will be issued upon successful completion. This allows you to share your credentials on social networking platforms, such as LinkedIn, Facebook and Twitter.


    Do I receive alumni status after completing this programme?

    No, there is no alumni status granted for this programme. In some cases, there are credits that count towards a higher level of certification. This information will be clearly noted in the programme brochure.


    How long will I have access to the learning materials?

    You will have access to the online learning platform and all the videos and programme materials for 12 months following the programme start date. Access to the learning platform is restricted to registered participants per the terms of agreement.

  • What equipment or technical requirements are there for this programme?

    Participants will need the latest version of their preferred browser to access the learning platform. In addition, Microsoft Office and a PDF viewer are required to access documents, spreadsheets, presentations, PDF files and transcripts.


    Do I need to be online to access the programme content?

    Yes, the learning platform is accessed via the internet, and video content is not available for download. However, you can download files of video transcripts, assignment templates, readings, etc. For maximum flexibility, you can access programme content from a desktop, laptop, tablet or mobile device.

    Video lectures must be streamed via the internet, and any livestream webinars and office hours will require an internet connection. However, these sessions are always recorded, so you can view them later.

  • Can I still register if the registration deadline has passed?

    Yes, you can register up until seven days past the published start date of the programme without missing any of the core programme material or learnings.


    What is the programme fee and what forms of payment do you accept?

    The programme fee is noted at the top of this programme web page and usually referenced in the programme brochure as well.

    • Flexible payment options are available (see details below as well as at the top of this programme web page next to FEE).
    • Tuition assistance is available for participants who qualify. Please email learner.success@emeritus.org.

    What if I don’t have a credit card? Is there another method of payment accepted?

    Yes, you can do the bank remittance in the programme currency via wire transfer or debit card. Please contact your programme advisor or email us at learner.success@emeritus.org for details.


    I was not able to use the discount code provided. Can you help?

    Yes! Please email us at learner.success@emeritus.org with the details of the programme you are interested in, and we will assist you.


    How can I obtain an invoice for payment?

    Please email learner.success@emeritus.org with your invoicing requirements and the specific programme you’re interested in enrolling in.


    Is there an option to make flexible payments for this programme?

    Yes, the flexible payment option allows a participant to pay the programme fee in instalments. This option is made available on the payment page and should be selected before submitting the payment.


    How can I obtain a W9 form?

    Please email us at learner.success@emeritus.org for assistance.

  • What is the policy on refunds and withdrawals?

    You may request a full refund within seven days of your payment or 14 days after the published start date of the programme, whichever comes later. If your enrolment had previously been deferred, you will not be entitled to a refund. Partial (or pro-rated) refunds are not offered. All withdrawal and refund requests should be sent to admissions@emeritus.org.



    What is the policy on deferrals?

    After the published start date of the programme, you have until the midpoint of the programme to request to defer to a future cohort of the same programme. A deferral request must be submitted along with a specified reason and explanation. Cohort changes may be made only once per enrolment and are subject to availability of other cohorts scheduled at our discretion. This will not be applicable for deferrals within the refund period, and the limit of one deferral per enrolment remains. All deferral requests should be sent to admissions@emeritus.org.

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