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MIT/ILP Calendar Event

Machine Learning for Big Data and Text Processing: Foundations

June 18-19, 2018

Overview

Machine learning methods drive much of modern data analysis across engineering, sciences, and commercial applications. For example, search engines, recommender systems, advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. Much of today's data is available in primarily textual form, requiring effective tools for using unstructured and semi-structured text. This course examines a suite of key machine learning tools and their applications, including predictive analysis. We will discuss key insights underlying the tools, what kinds of problems they can/cannot solve, how they can be applied effectively, and what issues are likely to arise in practical applications.

The course is designed to operate simultaneously on two levels, intuitive and more formal, describing key concepts, formulations, algorithms, and practical examples for professionals whose work interfaces data analysis in different ways and on different levels.

  • At the managerial level, the course provides the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data.
  • For professionals whose work involves data hands-on, the course aims to provide a deeper understanding and sharper intuitions about what is possible, what is not, and which methods to consider in what contexts.
  • For everyone, the course provides the ability to see problems as machine learning problems and be able to discuss ways to approach them.

Learning Objectives

  1. Understand broad opportunities for automation with machine learning
  2. Be able to formulate/set up problems as machine learning tasks
  3. Outline key aspects of practical problems that are likely to impact performance
  4. Assess which types of methods are likely to be useful for a given class of problems
  5. Understand strengths and weakness of "on-line" learning algorithms
  6. Be able to discuss scaling issues (amount of data, dimensionality, storage, and computation)
  7. See through the process of applying machine learning methods in practice, foresee likely hurdles and possible remedies
  8. Understand modern natural language processing tools, formulations, and problems
  9. Grasp what predictive analytics often does not provide
  10. Understand current machine learning trends and opportunities that they bring

Location

Cambridge, MA USA

MIT is located in Cambridge, Massachusetts. The campus, situated in close proximity to Boston’s Logan International Airport, profits from Boston’s excellent public transportation system and the on-campus Tech Shuttle. The closest subway station is Kendall Square, which acts as a commercial center for MIT and the local community.

Registration

FOR FURTHER INFORMATION OR TO REGISTER, CONTACT:

MIT Professional Education - Short Programs
Room E48-401
Cambridge, MA 02139
TEL: 617 253 2101 * FAX: 617 258 8831
e-mail: shortprograms@mit.edu

ILP members can save 15% on any MIT Professional Education short course - contact your ILO for details.