斯坦福大学开放课程 ：机器学习课程 Open Stanford Course : Engineering Everywhere-MachineLearning

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资源信息： 中文名: 斯坦福大学开放课程 ：机器学习课程 英文名: Open Stanford Course : Engineering Everywhere-MachineLearning 资源格式: WMV 发行日期: 2008年 地区: 美国 对白语言: 英语 文字语言: 英文 概述:

资源简介： 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 http://see.stanford.edu/see/images/CS229.jpg 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 http://see.stanford.edu/see/images/ayn.jpg 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. 资源中materials.rar是讲义，当然也是英文的。 不巧的是马上要过年了。大年三十开始到正月初七之间不保证持续供源。 正常情况下每天9：00~24：00都会开机。