Machine Learning: From Data to Decisions

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Course Dates: November 29, 2018 - February 13, 2019
Registration closes December 6, 2018

Duration: 2 Months (with a 2-week holiday break from Dec 23, 2019 - Jan 6, 2019)

Price: $1950 (Flexible Payment Options available. Learn more when you register.)

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Course Information

This program takes a look at machine learning through a lens of practical applications. It is designed specifically for decision makers who want to harness the power of machine learning and effectively manage the team of data scientists who make it possible.

By the end of the course, participants will be able to develop a competitive edge by turning what is unknown into what's known—leading to better business decisions and outcomes. Regardless of where you are on the spectrum of machine learning adoption---and, more broadly, artificial intelligence---this online program provides the latest thought leadership in machine learning tools and techniques.

Machine learning: From Data to Decisions requires no prerequisites in terms of math or computational sciences, although some basic experience with statistics is helpful.

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MIT Professional Education is working with Emeritus to deliver this online course. For additional course details, or to speak to a program advisor, please request more program information here:

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Key Takeaways

  • Gain insights from data using effective visualization tools
  • Improve your predictions by better identifying the right prediction models for your needs
  • Apply effective decision-making frameworks to make data-driven decisions
  • Evaluate your outcomes to determine how your decisions and actions impacted them

Who Should Attend

  • Decision makers across all business and technical functions will gain a practical understanding of the tools and techniques used in machine learning applications for business.
  • Examples from retail, ecommerce, financial services, healthcare, social media, advertising, technology, gaming, and pharmaceuticals are included in this online program. However, participants from all industries and sectors will find these examples relevant.
  • Functional and cross-functional teams are encouraged to attend together, to accelerate the machine learning adoption process.

What's in the Course

Module 1: Introduction and Overview of Machine Learning

Overview of the four building blocks of machine learning:

  • Understanding data: What is it telling us?
  • Prediction: What will happen?
  • Decision making: What to do?
  • Causal inference: Did it work?

Module 4: Prediction Part 2 – Classification

Classification is used to predict outcomes that fall into two or more categories, such as: male/female, yes/no, or red/blue/green.

  • Compare the ability of different methods to minimize prediction errors
  • Make better predictions, based on your data and desired outcome
  • Use the right approaches to deal with data complexity

Application: Spam filters, detecting malicious network connections, and predicting credit defaults among borrowers.

Module 2: Understanding Your Data

Learn the basic characteristics of data sets and identify effective statistical tools and visualizations to glean insights from your data.

  • Ask the right questions of the data
  • Know which tools to use to unlock insights
  • How data visualization clarifies data

Application: Wikipedia and Online Marketplaces, Amazon, and Etsy.

Module 5: Prediction Part 3 – Neural Networks

Neural networks are much like the networks in the human brain. They are used in machine learning to model complex relationships between inputs and outputs and to find patterns in data.

  • What are neural networks and how do they work?
  • Explore the history and examples of simple and complex neural networks
  • How neural networks minimize errors, regardless of the size of your data set

Application: Improving online search in ecommerce websites.

Module 3: Prediction Part 1 – Regression

Understand the basic concept of linear regression and how it can be used with historical data to build models that can predict future outcomes.

  • How to build a model that fits best with your data
  • How to quantify the degree of your uncertainty
  • What to do when you don’t have enough data
  • What lies beyond linear regression

Application: Designing a marketing campaign.

 

About the Instructor

Dr. Devavrat Shah
Professor of Electrical Engineering and Computer Science and Director of Statistics and Data Science Center

Devavrat Shah Devavrat Shah is a professor with the department of electrical engineering and computer science, MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC), and the Director of the newly formed Statistics and Data Center in Institute for Data, Systems, and Society. His research focus is on theory of large complex networks, which includes network algorithms, stochastic networks, network information theory, and large-scale statistical inference.