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.

    75%

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

    SOURCE: FORBES, JAN 2020

    $20.8B

    is the projected global ML market value by 2024

    SOURCE: ZION MARKET RESEARCH, NOV 2018

    75%

    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:

      • 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 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

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    Program Faculty

    Certificate

    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.

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