Imperial Machine Learning for Decision Making
No prior programming knowledge required


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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.


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



    is the projected global ML market value by 2024



    Investment in ML application in Q1 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.


    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


    Program Faculty


    Upon successful completion of the program, participants will be awarded a verified digital diploma by Emeritus Institute of Management, in collaboration with MIT Sloan, Columbia Business School Executive Education & Tuck School of Business at Dartmouth.