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机器学习课程-斯坦福大学开放课程

课程标题:机器学习课程-斯坦福大学开放课程

课程分类: 斯坦福大学

关键词TAG:斯坦福大学 公开课 机器学习 

课程主播: 斯坦福大学 机器学习 

播放次数:加载中...

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机器学习课程-斯坦福大学开放课程-课程简介

        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.
斯坦福大学开放课程:机器学习课程.第01集.机器学习的动机与应用.中英双字幕
斯坦福大学开放课程:机器学习课程.第02集.监督学习应用.梯度下降.中英双字幕
斯坦福大学开放课程:机器学习课程.第03集.欠拟合与过拟合的概念.中英双字幕
斯坦福大学开放课程:机器学习课程.第04集.牛顿方法.中英双字幕
斯坦福大学开放课程:机器学习课程.第05集.生成学习算法.中英双字幕
斯坦福大学开放课程:机器学习课程.第06集.朴素贝叶斯算法.中英双字幕
斯坦福大学开放课程:机器学习课程.第07集.最优间隔分类器问题.中英双字幕
斯坦福大学开放课程:机器学习课程.第08集.顺序最小优化算法.中英双字幕
斯坦福大学开放课程:机器学习课程.第09集.经验风险最小化.中英双字幕
斯坦福大学开放课程:机器学习课程.第10集.特征选择.中英双字幕
斯坦福大学开放课程:机器学习课程.第11集.贝叶斯统计正则化.中英双字幕
斯坦福大学开放课程:机器学习课程.第13集.高斯混合模型.中英双字幕
斯坦福大学开放课程:机器学习课程.第14集.主成分分析法.中英双字幕
斯坦福大学开放课程:机器学习课程.第15集.奇异值分解.中英双字幕
斯坦福大学开放课程:机器学习课程.第16集.马尔可夫决策过程.中英双字幕
斯坦福大学开放课程:机器学习课程.第17集.离散与维数灾难.中英双字幕
斯坦福大学开放课程:机器学习课程.第18集.线性二次型调节控制.中英双字幕
斯坦福大学开放课程:机器学习课程.第19集.微分动态规划.中英双字幕
斯坦福大学开放课程:机器学习课程.第20集.策略搜索.中英双字幕

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