Eric Grimson


Eric Grimson Bernard Gordon Professor of Medical Engineering, Professor of Computer Science MIT W. Eric L. Grimson is a Professor of Computer Science and Engineering, and the Bernard M. Gordon Professor of Medical Engineering. He was Chancellor of MIT from 2011 to 2014. A member of the MIT faculty since 1984, Professor Grimson previously served as head of the Department of Electrical Engineering and Computer Science, as its associate department head, and as its education officer. Professor Grimson is internationally recognized for his research in computer vision, especially in applications in medical image analysis. He and his students have developed techniques for activity and behavior recognition, object and person recognition, image database indexing, image guided surgery, site modeling, and many other areas of computer vision. Professor Grimson has been actively engaged with students throughout his career. For 25 years he lectured subject 6.001 Structure and Interpretation of Computer Programs, and is now engaged in teaching 6.00 Introduction to Computer Science and Programming and 6.01 Introduction to EECS. He has also taught undergraduate subjects in computer architecture, software engineering, and signal processing. In all, Professor Grimson has taught more than 10,000 MIT undergraduates and served as the thesis supervisor to almost 50 MIT PhDs. Professor Grimson is a native of Saskatchewan, Canada. He received the BSc (Hons) degree in mathematics and physics from the University of Regina in 1975 and his PhD in mathematics in 1980 from MIT. He is a recipient of the Bose Award for Excellence in Teaching in the School of Engineering at MIT. He is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and a fellow of the Institute of Electrical and Electronics Engineers (IEEE).

Introduction to Computer Science and Programming Using Python
About this course [[!]] [[]] This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features updated lecture videos, lecture exercises, and problem sets to use the new version of Python 3.5. Even if you took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning. Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not "computation appreciation" courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.
Eric Grimson 01-10 12:45