In this interactive activity from NOVA, learn about the sophisticated scientific instruments on two identical robotic rovers that have explored Mars Spirit and Opportunity.

" This course explores a range of contemporary scholarship oriented to the study of 'cybercultures,' with a focus on research inspired by ethnographic and more broadly anthropological perspectives. Taking anthropology as a resource for cultural critique, the course will be organized through a set of readings chosen to illustrate central topics concerning the cultural and material practices that comprise digital technologies. We'll examine social histories of automata and automation; the trope of the 'cyber' and its origins in the emergence of cybernetics during the last century; cybergeographies and politics; robots, agents and humanlike machines; bioinformatics and artificial life; online sociality and the cyborg imaginary; ubiquitous and mobile computing; ethnographies of research and development; and geeks, gamers and hacktivists. We'll close by considering the implications for all of these topics of emerging reconceptualizations of sociomaterial relations, informed by feminist science and technology studies."

This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. Upon successful completion of this course, the student will be able to: Describe the major applications, topics, and research areas of artificial intelligence (AI), including search, machine learning, knowledge representation and inference, natural language processing, vision, and robotics; Apply basic techniques of AI in computational solutions to problems; Discuss the role of AI research areas in growing the understanding of human intelligence; Identify the boundaries of the capabilities of current AI systems. (Computer Science 405)

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

" Welcome to 2.007! This course is a first subject in engineering design. With your help, this course will be a great learning experience exposing you to interesting material, challenging you to think deeply, and providing skills useful in professional practice. A major element of the course is design of a robot to participate in a challenge that changes from year to year. This year, the theme is cleaning up the planet as inspired by the movie Wall-E."

MIT high school students competitions in aerial design, robot design, robocraft programming, manufacturing and much more.

This is introductory module to the innovative research-oriented course of Bioinfomatics at the advanced undergraduate or beginnig graduate level. The course concerns computational and statistical methods for problems which are coming to the forefront of b

Selection of MIT OpenCourseWare content intended to stretch your creativity by learning how to build new things. See courses in action for Practical Electronics, Furniture Making, Building Landscapes, Robotics, Mechanical Engineering, Special Effects, Water Jet Technologies, and Toy Product Design.

Lego Robotics uses Legos as a fun tool to explore robotics, mechanical systems, electronics, and programming. This seminar is primarily a lab experience which provides students with resources to design, build, and program functional robots constructed from Legos and a few other parts such as motors and sensors.

This wiki page list the activities, student projects and journal reflections from the Math Bootcamp July 6-9, 2010 in Portola Valley, CA. The Boot Camp is an intensive, one-week program focused on accelerating the learning of some of the San Francisco Peninsula’s high potential young minds. It is directed at rising 6 -12th graders (we will be willing to make exceptions for particularly mature rising 5th graders) who demonstrate the motivation and ability to deepen their understanding of math, science and engineering. Although this is not an SAT-prep course, SAT-type problems will be heavily used and the students will be encouraged to take the SAT to assess their overall progress. The heavy thinking will be interspersed with creative and physical activities to ensure the flow of blood to both the body and brain!

The Mission to Mars curricular unit introduces students to Mars the Red Planet. Students discover why scientists are so interested in studying this mysterious planet. Many interesting facts about Mars are revealed, and the history of Martian exploration is reviewed. Students will learn about the development of robotics and how robots are beneficial to science, society and the exploration of space. Details on engineers' involvement in space exploration are presented. Furthermore, students will learn how orbits allow astronauts to move from planet to planet and what type of equipment is used by scientists and engineers to safely explore space. Lastly, the specific details on and human risks for a possible future manned mission to Mars (and back to Earth again!) are discussed.

MASLab (Mobile Autonomous System Laboratory) is a robotics contest. The contest takes place during MIT's Independent Activities Period and participants earn 6 units of P/F credit and 6 Engineering Design Points. Teams of three to four students have less than a month to build and program sophisticated robots which must explore an unknown playing field and perform a series of tasks. MASLab provides a significantly more difficult robotics problem than many other university-level robotics contests. Although students know the general size, shape, and color of the floors and walls, the students do not know the exact layout of the playing field. In addition, MASLab robots are completely autonomous, or in other words, the robots operate, calculate, and plan without human intervention. Finally, MASLab is one of the few robotics contests in the country to use a vision based robotics problem.

This module introduces the concept of free energy and potential fields in the context of protein conformation spaces and motion planning. It then provides examples of applications of motion planning techniques to problems from structural computational biology.

This lesson will start with a brief history of robotics and explain how robots are beneficial to science and society. The lesson then will explore how robots have been used in recent space exploration efforts. The engineering design of the two Mars rovers, Spirit and Opportunity, will be used as prime examples. Finally, the maneuverability of their robotic arms and the functionality of their tools will be discussed.

This module explains how to model a protein as a robotic manipulator and introduces the robotic path planning problem and algorithms for solving it, including the probabilistic roadmap method. Variations of the Probabilistic Roadmap Method have been employed for problems related to protein motion.

In this video from ThinkTV Dayton, a robotics engineer describes what robots do, the rewards and challenges of his job, a typical workday, the schooling that prepared him for his career, and the future of robotics.

This course offers a brief history of robotics, and a definition of robot and robotics. The course includes an introduction to IC and the XBC, downloading firmware,
Updating the bitstream, and IC Environment and simulator. It concludes with an activity building a Demo-Bot.

This course examines the issues, principles, and challenges toward building machines that cooperate with humans and with other machines. Philosophical, scientific, and theoretical insights into this subject will be covered, as well as how these ideas are manifest in both natural and artificial systems (e.g. software agents and robots).