Repeated motion is present everywhere in nature. Learn how to 'make waves' with your own movements using a motion detector to plot your position as a function of time, and try to duplicate wave patterns presented in the activity. Investigate the concept of distance versus time graphs and see how your own movement can be represented on a graph.

In the Mapping Earthquakes to Save the World activity, students leverage real-time data to plot earthquakes on a world map. The fate of the world is in their hands – the President of the United States has asked for their help to save humankind. Students identify patterns in their data and connect earthquakes with tectonic plates, making recommendations back to the President about where people are safe and where people are most at risk. This activity was heavily inspired by a project from the Stevens Institute for Technology Center for Innovation in Engineering and Science Education.

In the Mapping Earthquakes to Save the World activity, students leverage real-time data to plot earthquakes on a world map. The fate of the world is in their hands – the President of the United States has asked for their help to save humankind. Students identify patterns in their data and connect earthquakes with tectonic plates, making recommendations back to the President about where people are safe and where people are most at risk. This activity was heavily inspired by a project from the Stevens Institute for Technology Center for Innovation in Engineering and Science Education.

Understanding the brain’s remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.

6.637 covers the fundamentals of optical signals and modern optical devices and systems from a practical point of view. Its goal is to help students develop a thorough understanding of the underlying physical principles such that device and system design and performance can be predicted, analyzed, and understood.
Most optical systems involve the use of one or more of the following: sources (e.g., lasers and light-emitting diodes), light modulation components (e.g., liquid-crystal light modulators), transmission media (e.g., free space or fibers), photodetectors (e.g., photodiodes, photomultiplier tubes), information storage devices (e.g., optical disk), processing systems (e.g., imaging and spatial filtering systems) and displays (LCOS microdisplays). These are the topics covered by this course.

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Students apply several methods developed to identify and interpret patterns to the identification of fingerprints. They look at their classmates' fingerprints, snowflakes, and "spectral fingerprints" of elements. They learn to identify each image as unique, yet part of a group containing recognizable similarities.