Course description

Predictive Analytics and Machine Learning

Introduction

This course teaches students how to use machine learning techniques and tools for predictive analytics. The course will also teach students to: specify problems accessible with these methods, evaluate analytics models, use new tools in the rapidly evolving automated machine learning field, and deploy machine learning models. The course assumes prior experience with: basic probability theory, traditional methods for analysis of continuous and categorical variables, and introductory Python programming.

Course content

This course focuses on predictive analytics and machine learning applications: supervised learning (classification and regression). The course will also introduce unsupervised learning, including clustering, market basket analysis, and dimensionality reduction. Students will implement and tune individual machine learning algorithms (e.g., via Python’s scikit-learn library), and work with tools in the rapidly evolving field of automated machine learning. In addition to lecture, the course will include cases, in-class workshops, and guest speakers as appropriate.

Learning outcome knowledge

Students will learn about machine learning algorithms, translating business problems into problems machines can solve, and evaluating machine learning models. Students will also learn about recent advances in automated machine learning, and the implications for generalized deployment of the technology. Students will extend their programming expertise to machine learning applications, using predictive analytics to address business problems and to evaluate strategic options. Students will become familiar with automated machine learning tools such as: Python’s auto_ml and tpot libraries, DataRobot, exploratory.io, etc. Because these automated ML tools are very new and changing very rapidly, final decisions on tools will depend on the “state of the art” when the course starts.

Exam organisation

  • Home exam: 50%
  • Written exam: 50%