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Gang home reading bulkairmail guide core courses in linear algebra core problem of linear algebra strong play discs recommended articles Further reading teaching linear algebra matrix theory study guide questions job learning resources weekly problem sets of data sufficiency questions IES eigenvalues and eigenvectors Special bulkairmail matrix matrix factorization Hermitian / real symmetric matrix singular value decomposition of the thematic bulkairmail topics Jordan typical form of thematic Fourier analysis of thematic maps on the topic of machine learning Markov chain thematic topics of linear programming topics Differential / differential equations math topics thematic combination of linear bulkairmail equations with the aid of matrix algebra query vector linear transformation in the space product space determinant eigenvalues and eigenvectors and quadratic forms typical FAQ Chou time teaching CD on the message board of machine learning is nearly 20 years the rise of a more interdisciplinary field involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and many other subjects. Machine learning theory is to design and analyze some let the computer can automatically "learn" algorithms. Machine learning algorithms are a class of automated analysis of data obtained from the law, and the law on the use of algorithms to predict unknown data. Because learning algorithms involved in a large number of statistical theory, machine learning and statistical inference learning particularly close contact, also known as statistical learning theory. Algorithm design, machine learning theory focuses on achievable and effective learning algorithms. Many inference problems are no procedures to follow difficult, so part of machine learning research is to develop tractable approximation algorithms.
To enable the computer to solve the problem, we need a algorithm. If I want to extract linear equations, Gaussian elimination algorithm that is applicable. But many practical bulkairmail problems often lack accurate and effective algorithm, for example, to detect spam (spam), which is to distinguish which messages are "uninvited, forced into a mailbox of junk mail", then you can come in machine learning handy. Because the content of spam often contains adult advertising, commercial sites or personal bulkairmail website advertising, make money information, chain letters, most anti-spam bulkairmail system will be based on the content of the message to distinguish discrimination. Suppose I set out to design a set of individuals dedicated anti-spam system: First, according to the proportion of spam and normal mail messages, such as 5: 4, I put together a sample of "training materials," junk mail assuming 100 and 80 letters normal mail. The next step is for each seal training messages and extracted feature analysis, word frequency calculation is the most common method. Then I picked up from a number of classification bulkairmail systems in a classifier, such as classification tree, and then start the learning process, the purpose is to find really useful features and get the best set of classification rules, the rules may be generated as follows: "If bulkairmail the" free "The word frequency bulkairmail greater than 3%, and the" easy money "appears at least twice, then classified as spam." Eventually, the effectiveness of the analysis carried out for another "test data" computing abandoned bulkairmail true (normally be mistaken bulkairmail for junk mail mail) the proportion of the number, take the pseudo (spam sentenced for normal mail) the proportion of how much. The system is constructed above process has been successfully developed many applications, such as Wikipedia described [1]:
However, we do not optimistic that someday machine learning bulkairmail can not learn nothing like humans. Professor, Department of Philosophy, University of California, Berkeley, USA Dreyfus (Hubert L. Dreyfus) published "computer still can do" (What Computers Still Can not Do) in 1972, with the title is criticized "artificial bulkairmail reasoning "(A Critique of Artificial Reason), argues that artificial intelligence can not mimic the human higher-order mental functions. After the book came immediately caused a stir in academic artificial intelligence, along with widespread criticism and lambasting. Still, there are a lot of people quietly reading the book, I was one of them. Every now and again, I always take out the shelves this has long been the forgotten little yellowed books, feel free to browse bulkairmail read. Reproduces some of the quotes below 赛尔弗里奇 (Oliver G. Selfridge) and Neisseria (Ulric Neisser) 50 years ago, the two papers <pattern recognition machine> (Pattern Recognition by Machine, see Computers and Thought, 1963, p. 250) in which some application on the [2]:
"The most important learning process of all is still untouched: No current program can generate test features of its own The effectiveness of all of them is forever restricted by the ingenuity or arbitrariness of their programmers We can barely guess how this restriction might be.. overcome. Until it is, 'artificial intelligence' will remain tainted with artifice. "
Reference Source: [1] http://zh.wikipedia.org/wiki/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0 [2] Hubert, L. Dreyfus , What Computers Still Can not Do, MIT Press, Third Printing, 1993. [3] Erwin Brecher, Journey Through Puzzleland, Pan Books, 1994.
Reply cited above Wikipedia application examples and more models available random statements, and therefore belong to the category bulkairmail of machine learning to solve. But I'll give two examples of seemingly random that it is not true, that there is certainty of (deterministic) or generate classification rules.
These two questions taken from a puzzle bulkairmail book, the author, introducing the second question, said: "I heard from a Mensa member of the puzzle there, many years ago while he was held in the Netherlands on mathematics conference heard. Later, I relayed bulkairmail the question to a friend, he was unable to solve, they put a question to bring the club to which he belongs. members spent several hours still did not succeed. Finally, a question to get home to tell members turn 12 years old son, his son took five minutes to find the answer. "These two issues are of this type of problem.
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