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简介：《斯坦福大学网络视频课程之机器人学》(Introduction to robotics)
《斯坦福大学-机器学习课程》(Stanford Engineering Everywhere-MachineLearning)
中文名: 斯坦福大学网络视频课程之机器人学
英文名: Introduction to robotics
发行时间: 2008年
地区: 美国
对白语言: 英语
简介:
The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.
The course is presented in a standard format of lectures, readings and problem sets. There will be an in-class midterm and final examination. These examinations will be open book. Lectures will be based mainly, but not exclusively, on material in the Lecture Notes book. Lectures will follow roughly the same sequence as the material presented in the book, so it can be read in anticipation of the lectures
Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design.
涉及内容：机器人运动学、动力学、控制、运动规划、编程及涉及等。
Oussama Khatib
Khatib"s current research is in human-centered robotics, human-friendly robot design, dynamic simulations, and haptic interactions. His exploration in this research ranges from the autonomous ability of a robot to cooperate with a human to the haptic interaction of a user with an animated character or a surgical instrument. His research in human-centered robotics builds on a large body of studies he pursued over the past 25 years and published in over 200 contributions in the robotics field.
Prof. Khatib was the Program Chair of ICRA2000 (San Francisco) and Editor of ``The Robotics Review"" (MIT Press). He has served as the Director of the Stanford Computer Forum, an industry affiliate program. He is currently the President of the International Foundation of Robotics Research, IFRR, and Editor of STAR, Springer Tracts in Advanced Robotics. Prof. Khatib is IEEE fellow, Distinguished Lecturer of IEEE, and recipient of the JARA Award.
很不错的机器人学课程，这个课程是斯坦福大学今年发布到网上的视频教程，要知道机器人学这门课在有网络课程之前，每年只有斯坦福大学的学生才有机会听到，人数不到1万！！课程主要是针对机器人学的各个方面进行一个全方位系统的讲解，包括运动学、动力学建模，运动规划，跟踪，设计，控制仿真，机器人编程等方面。如果能够耐心看完这个课程（当然前提条件是你能够听懂！），你会对机器人系统有一个全面的认识和了解！
Oussama Khatib 教授是机器人方面的专家，他是麻省理工The Robitics Review的撰稿人，IFRR的主席，IEEE fellow，并且还获得过JARA奖。
不过由于是英语授课，而且Oussama Khatib 教授有一点口音，所以可能对英语水平的要求稍微要高一点点O(∩_∩)。同时课程涉及到很多专业方面的词汇和概念，所以推荐给相关专业和领域的朋友。当然，所有对机器人学、机器人系统感兴趣的朋友都可以下载来看看！
附上斯坦福在线课程的网址，提供youtube的在线观看以及mp4和wmv的bt下载，不愿意用电驴的朋友可以选择别的方式下载。
http://see.stanford.edu/SEE/Courses.aspx
补充上每一课对应的PDF文件，类似发言稿，听不懂的朋友可以当成字幕看了，呵呵！
中文名: 斯坦福大学-机器学习课程
英文名: Stanford Engineering Everywhere-MachineLearning
发行时间: 2008年
地区: 美国
对白语言: 英语
文字语言: 英文
简介:
相对于其他名校，斯坦福大学的工科课程更注重实用性。这也是我个人很赞赏的一点。
关于发布本资源的初衷。坦白的说，人工智能的发展到已经进入了一个瓶颈期。近年来各个研究方向都没有太大的突破。真正意义上人工智能的实现目前还没有任何曙光。但是，机器学习无疑是最有希望实现这个目标的方向之一。斯坦福大学的“Stanford Engineering Everywhere ”免费提供学校里最受欢迎的工科课程，给全世界的学生和教育工作者。得益于这个项目，我们有机会和全世界站在同一个数量级的知识起跑线上。
此课程献给所有同好。让我们向着朝阳奔跑吧~
本课程来源于斯坦福大学的“Stanford Engineering Everywhere ”项目。
首页为：http://see.stanford.edu/default.aspx
目前已有的课程是：
Introduction to Computer Science:
Programming Methodology CS106A
Programming Abstractions CS106B
Programming Paradigms CS107
Artificial Intelligence:
Introduction to Robotics CS223A
Natural Language Processing CS224N
Machine Learning CS229
Linear Systems and Optimization:
The Fourier Transform and its Applications EE261
Introduction to Linear Dynamical Systems EE263
Convex Optimization I EE364A
Convex Optimization II EE364B
本课程为Artificial Intelligence里的Machine Learning CS229
课程简介：
Artificial Intelligence | Machine Learning
Instructor: Ng, Andrew
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.
Students are expected to have the following background:
Prerequisites: - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
讲师简介：
Andrew Ng
Ng"s research is in the areas of machine learning and artificial intelligence. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.
Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. Using this approach, Ng"s group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. As part of this work, Ng"s group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles.
《斯坦福大学-机器学习课程》(Stanford Engineering Everywhere-MachineLearning)
中文名: 斯坦福大学网络视频课程之机器人学
英文名: Introduction to robotics
发行时间: 2008年
地区: 美国
对白语言: 英语
简介:
The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.
The course is presented in a standard format of lectures, readings and problem sets. There will be an in-class midterm and final examination. These examinations will be open book. Lectures will be based mainly, but not exclusively, on material in the Lecture Notes book. Lectures will follow roughly the same sequence as the material presented in the book, so it can be read in anticipation of the lectures
Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design.
涉及内容：机器人运动学、动力学、控制、运动规划、编程及涉及等。
Oussama Khatib
Khatib"s current research is in human-centered robotics, human-friendly robot design, dynamic simulations, and haptic interactions. His exploration in this research ranges from the autonomous ability of a robot to cooperate with a human to the haptic interaction of a user with an animated character or a surgical instrument. His research in human-centered robotics builds on a large body of studies he pursued over the past 25 years and published in over 200 contributions in the robotics field.
Prof. Khatib was the Program Chair of ICRA2000 (San Francisco) and Editor of ``The Robotics Review"" (MIT Press). He has served as the Director of the Stanford Computer Forum, an industry affiliate program. He is currently the President of the International Foundation of Robotics Research, IFRR, and Editor of STAR, Springer Tracts in Advanced Robotics. Prof. Khatib is IEEE fellow, Distinguished Lecturer of IEEE, and recipient of the JARA Award.
很不错的机器人学课程，这个课程是斯坦福大学今年发布到网上的视频教程，要知道机器人学这门课在有网络课程之前，每年只有斯坦福大学的学生才有机会听到，人数不到1万！！课程主要是针对机器人学的各个方面进行一个全方位系统的讲解，包括运动学、动力学建模，运动规划，跟踪，设计，控制仿真，机器人编程等方面。如果能够耐心看完这个课程（当然前提条件是你能够听懂！），你会对机器人系统有一个全面的认识和了解！
Oussama Khatib 教授是机器人方面的专家，他是麻省理工The Robitics Review的撰稿人，IFRR的主席，IEEE fellow，并且还获得过JARA奖。
不过由于是英语授课，而且Oussama Khatib 教授有一点口音，所以可能对英语水平的要求稍微要高一点点O(∩_∩)。同时课程涉及到很多专业方面的词汇和概念，所以推荐给相关专业和领域的朋友。当然，所有对机器人学、机器人系统感兴趣的朋友都可以下载来看看！
附上斯坦福在线课程的网址，提供youtube的在线观看以及mp4和wmv的bt下载，不愿意用电驴的朋友可以选择别的方式下载。
http://see.stanford.edu/SEE/Courses.aspx
补充上每一课对应的PDF文件，类似发言稿，听不懂的朋友可以当成字幕看了，呵呵！
中文名: 斯坦福大学-机器学习课程
英文名: Stanford Engineering Everywhere-MachineLearning
发行时间: 2008年
地区: 美国
对白语言: 英语
文字语言: 英文
简介:
相对于其他名校，斯坦福大学的工科课程更注重实用性。这也是我个人很赞赏的一点。
关于发布本资源的初衷。坦白的说，人工智能的发展到已经进入了一个瓶颈期。近年来各个研究方向都没有太大的突破。真正意义上人工智能的实现目前还没有任何曙光。但是，机器学习无疑是最有希望实现这个目标的方向之一。斯坦福大学的“Stanford Engineering Everywhere ”免费提供学校里最受欢迎的工科课程，给全世界的学生和教育工作者。得益于这个项目，我们有机会和全世界站在同一个数量级的知识起跑线上。
此课程献给所有同好。让我们向着朝阳奔跑吧~
本课程来源于斯坦福大学的“Stanford Engineering Everywhere ”项目。
首页为：http://see.stanford.edu/default.aspx
目前已有的课程是：
Introduction to Computer Science:
Programming Methodology CS106A
Programming Abstractions CS106B
Programming Paradigms CS107
Artificial Intelligence:
Introduction to Robotics CS223A
Natural Language Processing CS224N
Machine Learning CS229
Linear Systems and Optimization:
The Fourier Transform and its Applications EE261
Introduction to Linear Dynamical Systems EE263
Convex Optimization I EE364A
Convex Optimization II EE364B
本课程为Artificial Intelligence里的Machine Learning CS229
课程简介：
Artificial Intelligence | Machine Learning
Instructor: Ng, Andrew
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.
Students are expected to have the following background:
Prerequisites: - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
讲师简介：
Andrew Ng
Ng"s research is in the areas of machine learning and artificial intelligence. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.
Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. Using this approach, Ng"s group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. As part of this work, Ng"s group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles.
