v

您的位置:VeryCD教育理工科


《斯坦福大学开放课程 :机器学习课程》(Open Stanford Course : Engineering Everywhere-MachineLearning)[WMV]

  • 状态: 精华资源
  • 摘要:
    发行日期2008年
    对白语言英语
    文字语言英文
  • 时间: 2009/01/22 12:35:32 发布 | 2009/01/23 12:10:20 更新
  • 分类: 教育  理工科 

autorta

精华资源: 1

全部资源: 1

相关: 分享到新浪微博   转播到腾讯微博   分享到开心网   分享到人人   分享到QQ空间   订阅本资源RSS更新   美味书签  subtitle
该内容尚未提供权利证明,无法提供下载。
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
简介

IPB Image

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。

正在读取……

这里是其它用户补充的资源(我也要补充):

暂无补充资源
正在加载,请稍等...

点击查看所有155网友评论

 

(?) [公告]留口水、评论相关规则 | [活动]每日签到 轻松领取电驴经验

    小贴士:
  1. 类似“顶”、“沙发”之类没有营养的文字,对勤劳贡献的楼主来说是令人沮丧的反馈信息。
  2. 提问之前请再仔细看一遍楼主的说明,或许是您遗漏了。
  3. 勿催片。请相信驴友们对分享是富有激情的,如果确有更新版本,您一定能搜索到。
  4. 请勿到处挖坑绊人、招贴广告。既占空间让人厌烦,又没人会搭理,于人于己都无利。
  5. 如果您发现自己的评论不见了,请参考以上4条。