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Discrete Choice Analysis: Predicting Demand and Market Shares

June 18-22, 2018

This one-week program undertakes an in-depth study of discrete choice models (logit, nested logit, generalized extreme value, probit, logit mixtures), data collection, specification, estimation, statistical testing, forecasting, and application. The covered topics include analysis of revealed and stated preferences data, sampling, and simulation-based estimation, discrete panel data, Bayesian estimation, discrete-continuous models, menu choice, and models with latent variables. The course includes practical application sessions where participants will be provided with discrete choice software to learn how to estimate and test discrete choice models taught in lecture using real databases, and gain hands on experience in using new discrete choice techniques for practical applications. By examining actual case studies of discrete choice methods, students will be familiarized with problems of model formulation, testing, and forecasting.

Discrete choice models are widely used for the analysis of individual choice behavior and can be applied to choice problems in many fields such as economics, environmental management, urban planning, etc. For example, discrete choice modeling is used in marketing research to guide product positioning, pricing, product concept testing, and many other areas of strategic and tactical interest. Recent applications to predict changes in demand and market shares include areas such as choice of travel mode, coffee brand, telephone service, soft drinks and other foods, and choice of durables such as automobiles, air conditioners, and houses.

Who Should Attend

This program is intended for academics and professionals interested in learning new discrete choice techniques and how to predict choice and forecast demand. They will gain hands-on experience in applying discrete choice software in real-world case studies. Participants need only have a basic working knowledge of statistical methods.

High-Speed Imaging for Motion Analysis: Systems and Techniques

June 18-21, 2018

This program is designed for scientists, engineers, and photographers who need to gather data on rapidly moving subjects and events for study, motion analysis, and trouble-shooting. Mornings are spent in the lecture hall learning the fundamentals for lighting, imaging technologies, and motion analysis. Afternoons are spent making high-speed images in the laboratory. In addition to carrying out the standard techniques, attendees will try out the latest in high-speed-imaging equipment, with the manufacturer's representatives there to provide hands-on education and experience with the systems. The course is held at the Edgerton Center at MIT - the home of Doc Edgerton's Stroboscopic Light Laboratory, where much of the history of the field was written.

With support from the leading manufacturers and consultants, this program features the broadest experience available anywhere in the fields of high-speed film and high-speed electronic imaging capture and analysis. Of particular interest is the latest trend of merging high-speed electronic images and instrumentation data for in-depth analysis of mechanical events.

The scope of the program should make it invaluable to anyone who wishes to broaden their capabilities in the field of high-speed imaging.

Machine Learning for Big Data and Text Processing: Foundations

June 18-19, 2018

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

Organizations, Innovation, and Technology: Putting Ideas to Work

June 18-22, 2018

Innovation typically begins with a new technical concept or other bright idea. But the new idea is just the first step on the long path to successful innovation. Technical change usually requires organizational changes as well. These changes include providing resources for technical development and acquiring the support of others in the organization or in outside organizations. Gaining this support requires negotiation, bargaining, and coalition building. Organizational change, then, is a very complex process. Change of this sort can be very difficult. Significant innovations can be resisted, fall victim to competing ideas, or fail to be sustained.

Thus innovators need their original idea and a vision of how the world will change if the innovation succeeds. But the real bottleneck in achieving success is the organizational change needed for implementing that idea. This course focuses on strategies to overcome the bottlenecks: how to build the needed coalition of supporters who will enable the necessary organizational change. This change process is not captured by simple cookbook procedures, so we will investigate a variety of detailed, original case studies, rich in lessons for innovation success and failure. The cases are drawn from many sectors, public and private, from the U.S. and other countries. We will also explore the diversity of innovation experiences of the class participants.

In evaluating organizational innovation strategies, there are obvious differences between public and private sector organizations. Yet while the incentives are often very different, the underlying processes of innovation are very similar in the two sectors. We are particularly interested in public - private interactions. Successful innovation strategies in the private sector often involve effective exploitation of public organizations, while public innovation usually requires mobilization of support from the private sector.

Who Should Attend

Private and public managers, consultants, and academics who are working to promote and sustain innovations through organizational change.

Other typical participants came from Samsung, Corning, Shell, Intel, Siemens, Northrop Grumman, Monsanto, Toyota, Halliburton, EMC, the Office of the Secretary of Defense and numerous military labs, Allianz SE, MITRE, Akamai Technologies, the CIA, Deere & Co., Booz Allen Hamilton, the European Commission, and other firms, government agencies, and universities around the world.

Rapid Prototyping Technology

June 18-22, 2018

Participants will obtain hands-on exposure to processes commonly used to rapidly fabricate prototypes. Classroom time covers an introductory-level review of the principles that govern the technologies, design for manufacturing, and best practices. Laboratory time includes design of representative components, observation of fabrication by MIT staff, and measurement/inspection of the resulting parts. The course materials cover 3D printing, laser cutting (polymers), waterjet cutting (metals and polymers), CNC milling (metals and polymers), CNC turning (metals and polymers), thermoforming (polymers), silicone molding, and use of a CNC router (wood and/or foam).

Who Should Attend

This course is directed at individuals who need to understand what dominates and limits the capabilities of the rapid prototype fabrication processes that will be covered in the class, and is designed for professionals that are looking to gain knowledge and insight that enables them to select appropriate processes/technologies and then make good design/fabrication/assembly decisions when utilizing the processes. The course would be useful for individuals seeking to better understand how to prototype items, for example: designers, design engineers, directors of engineering, technicians, researchers, makers, model builders, and hobbyists. The lessons learned are highly useful in fields related to design, manufacturing, the arts, architecture, and R&D.

Prerequisite Skills/Knowledge

A technical background including trigonometry and freshman-level physics will enable participants to more fully understand the principles, i.e. the "why," of that which governs the capabilities and limitations of the processes.

Advanced Machine Learning for Big Data and Text Processing

June 20-22, 2018

Machine learning methods drive much of modern data analysis across engineering, science, 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. This course looks at how the latest tools, techniques, and algorithms driving modern and predictive analysis can be applied in different fields, even when using unstructured data. You'll gain insights about the underlying 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, particularly in the healthcare field.

Applied Cybersecurity

June 25, 2018 to June 29, 2018

In today’s world, organizations must be prepared to defend against threats in cyberspace. Decision makers must be familiar with the fundamental principles and best practices of cyber security to best protect their enterprises. In this course, experts from academia, the military, and industry share their knowledge to give participants the principles, the state of the practice, and strategies for the future.

Sessions will address information security, ethical and legal practices, and mitigating cyber vulnerabilities. Participants will also learn about the process of incident response and analysis. The content is targeted at ensuring the privacy, reliability, and integrity of information systems.

The majority of the course (about 75%) is geared toward participants at the decision-making level who need a broad overview, rather than those who are already deeply immersed in the technical aspects of cyber security (software development, digital forensics, etc.), although both groups will find the course valuable.

Cyber security is a very large subject. This course is only intended to cover the fundamentals of the current leading and pressing cyber security topics. The result is that we can cover many different approaches. We cover the introduction of a topic and after the fundamentals, you can explore further on your own. The goal is for participants to understand the utility of each topic, not to become specialists in any one subject.

Who Should Attend
75% of the course is geared toward providing a basic framework for professionals making cyber security decisions in industry and government and individuals seeking to immerse themselves in the pressing issues of cyber security, giving them the information they need to make the best decisions for the defense of their organizations. About a quarter of the course covers more technical areas of interest to those with more engineering-focused backgrounds, such as software developers or those working in digital forensics. Although those with a computing background would be better prepared for the more technical topics, an engineering or computing background is not required to benefit from any of the sessions.

Beyond Smart Cities

June 25, 2018 to June 27, 2018

The world is experiencing a period of extreme urbanization. In China alone, more than 250 million rural inhabitants will move to urban areas over the next 15 years. This will require building new infrastructure to accommodate nearly the equivalent of the current population of the United States in a matter of a few decades. Cities in the 21st century will account for nearly 90% of global population growth, 80% of wealth creation, and 60% of total energy consumption. It is a global imperative to develop systems that improve the livability of cities while dramatically reducing resource consumption. This course will focus on understanding the complexities of cities through the use of Big Data Urban Analytics and the design of New Urban Systems for high-density cities such as systems for mobility, energy, food, and living/working. The design of these systems must be resilient, scalable, and reconfigurable.

Today, academic research and industrial applications in the area of “Smart Cities” seek to optimize existing city infrastructure, networks, and urban behavior through the deployment and utilization of digital networks. Cities that employ optimization techniques have reported improvements in energy efficiency, water use, public safety, road congestion, and many other areas. However, optimization has its limits. For instance, the improvement of traffic flow in most cities can approach 10% based on current “Smart Cities” approaches such as sensing the road network, predicting the demand, and controlling traffic signaling. Research and investments in new urban systems are fundamentally critical because optimization will have little effect for rapidly urbanizing cities such as Bangalore, India, which experience around the clock congestion. This course moves beyond “Smart Cities” by focusing on disruptive innovations in technology, design, planning, policy, and strategies that can bring dramatic improvements in urban livability and sustainability.

This course aims to develop a holistic model for high-performance urban living based on the concept of “Compact Urban Cells” – a neighborhood area of approximately one square kilometer in diameter containing most of what citizens need for everyday life within a 20-minute walk. This course will introduce the following key elements for Compact Urban Cells:

  1. Resilient Urban Cells – compact, walkable neighborhood where places of living, work, culture, shopping, and play are within short reach and support a rich diversity of interactions and activities.
  2. New Mobility Systems – alternatives to the private fossil-fueled automobile are more convenient, affordable, pleasurable, and traffic congestion can be essentially eliminated: Electric-based and shared options.
  3. Resilient Energy Systems – microgrids, and locally-produced renewables create agile, adaptable, efficient energy networks.
  4. Living Space on Demand – hyper-efficient and transformable micro-apartments that are affordable, fun and productive for young professionals in the creative heart of the city.
  5. Shared Co-Working Facilities – co-working facilities, cafés, "fab labs" (fabrication laboratories), and other shared facilities support innovation and entrepreneurship.
  6. Urban Food Production – advanced urban agriculture systems integrated onto rooftops and façades of buildings efficiently deliver high-quality produce and help solve food security problems.
  7. Responsive Technologies – innovative systems enable powerful new applications that improve the life of each resident in areas of health, energy conservation, mobility, and communications.
  8. Trust Networks – privacy is assured for otherwise invasive systems that make use of highly personal data such as mobility patterns and resource consumption (food, water, energy, and individual health profiles).

The course will be divided into three learning methods 1) lectures by course faculty and guests from academia and industry, 2) participatory group design work in “charrette” sessions (a type of brainstorming), and 3) critique by faculty and invited experts. Using the MIT campus and the Kendall Square area as a potential site for deployment, course participants will work on a series of short in-class assignments that focus on solving practical urban problems. The goal of the workshop is for participants to engage in critical thinking about the technological, social, cultural, and economic challenges for achieving smart sustainable cities in order to return to their community, corporation, or institution to implement positive change.

Who Should Attend

This program is designed for executives, business unit leaders and managers, financial investors and entrepreneurs, engineers/designers, and urban planners, from companies focused on the built environment, personal mobility and transit, energy, IT infrastructure, food, and Smart Cities development.

This program is also designed for government leaders charged with new urban economic development, design of new cities, and urban innovation districts or zones. Participants may include government leaders (e.g. mayors or vice-mayors), ministry and agency leaders, department directors, innovation managers, policymakers, city planners, and civil servants at the city, state, regional, or federal level. This course is open to government leaders in the U.S. and internationally.