everything.study
15.071

The AI Edge

4-0-8 units · Graduate

Presents methods and applications of data science, artificial intelligence, and generative AI. Covers the core toolkit of machine learning ¿ regularized regression and classification, time series, tree-based methods ¿ and deep learning ¿ neural networks, transformers, large language models, and multi-modal AI. Follows a problem-driven approach using case studies in supply chains, finance, transportation, energy, healthcare, and digital platforms. Intended for students who aim to understand the foundations underpinning the AI revolution, design and deploy designing that create operational and strategic value, and lead AI projects in organizations. Includes a team project focused on designing an end-to-end AI solution and communicating results to technical and executive audiences. Prior familiarity with Python and foundational machine learning concepts recommended. Students taking graduate version complete additional assignments.

Spring 2017 · The Analytics Edge

This course presents real-world examples in which quantitative methods provide a significant competitive edge that has led to a first order impact on some of today's most important companies. We outline the competitive landscape and present the key quantitative methods that created the edge (data-mining, dynamic optimization, simulation), and discuss their impact.

Readings

  • [AE] = Dimitris Bertsimas, Allison O’Hair and Bill Pulleyblank, The Analytics Edge , Dynamic Ideas, 2016. ISBN: 978-0989910897.
    Listed reading
  • Topics: 1.2 Introduction to Analytics · Readings: [AE] Chapter 11.
    Reading assignment
  • Topics: 2.2 Predicting the Quality of Wine · Readings: [AE] Chapter 1.1, 21.1.
    Reading assignment
  • Topics: 2.3 Moneyball: Sport Analytics · Readings: [AE] Chapter 4.
    Reading assignment
  • Topics: 3.3 The Framingham Heart Study · Readings: [AE] Chapter 7, 22.2.
    Reading assignment
  • Topics: 4.2 Forecasting Supreme Court Decisions · Readings: [AE] Chapter 1.3, 21.3.
    Reading assignment
  • Topics: 4.3 Predicting Healthcare Costs · Readings: [AE] Chapter 8.
    Reading assignment
  • Topics: 5.2 Social Network Analytics · Readings: [AE] Chapter 16.
    Reading assignment
  • Topics: 5.3 IBM Watson · Readings: [AE] Chapter 3.
    Reading assignment
  • Topics: 6.2 Recommendation Systems · Readings: [AE] Chapter 13.
    Reading assignment
  • Topics: 6.3 Clustering for Customer Segmentation and Patterns of Heart Attacks · Readings: [AE] Chapter 9.1, 21.4.
    Reading assignment
  • Topics: 8.3 Radiation Therapy · Readings: [AE] Chapter 22.7.
    Reading assignment
  • Topics: 8.4 Adwords Optimization · Readings: [AE] Chapter 12.
    Reading assignment
  • Topics: 9.2 Revenue Management and Sports Scheduling · Readings: [AE] Chapter 5, 17, 22.6.
    Reading assignment
  • The readings include chapters from the required book for the class:
    Listed reading
  • Dimitris Bertsimas, Allison O’Hair and Bill Pulleyblank, The Analytics Edge , Dynamic Ideas, 2016. ISBN: 978-0989910897.
    Listed reading

Prerequisites & course information

Prereq: 15.060 or permission of instructor G (Fall, Spring) 4-0-8 units Credit cannot also be received for 15.0711

Sources