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斯坦福大學-機器學習課程Stanford Engineering Everywhere-MachineLearning 英文版 DVD 只能於電腦播放


相對於其他名校,斯坦福大學的工科課程更注重實用性。這也是我個人很讚賞的一點。
關於發布本資源的初衷。坦白的說,人工智能的發展到已經進入了一個瓶頸期。近年來各個研究方向都沒有太大的突破。真正意義上人工智能的實現目前還沒有任何曙光。但是,機器學習無疑是最有希望實現這個目標的方向之一。斯坦福大學的“Stanford Engineering Everywhere ”免費提供學校裡最受歡迎的工科課程,給全世界的學生和教育工作者。得益於這個項目,我們有機會和全世界站在同一個數量級的知識起跑線上。

此課程獻給所有同好。讓我們向著朝陽奔跑吧~

本課程來源於斯坦福大學的“Stanford Engineering Everywhere ”項目。
首頁為:http://see.stanford.edu/default.aspx
目前已有的課程是:
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

本課程為Artificial Intelligence裡的Machine Learning CS229

課程簡介:
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.)

講師簡介:
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.

資源中materials.rar是講義,當然也是英文的。
 

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