What are the different types of learning in Artificial intellilgence Explain with example?
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As a broad subfield of artificial intelligence, Machine learning is concerned with the development of algorithms and techniques, which allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods create computer programs by extracting rules and patterns out of massive data sets. It should be noted that although pattern identification is important to Machine Learning, without rule extraction a process falls more accurately in the field of data mining.\n\nMachine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations. Many inference problems turn out to be NP-hard or harder, so part of machine learning research is the development of tractable approximate inference algorithms.\n\nMachine learning has a wide spectrum of applications including search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.
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the type of learning principle that is based on the assumption that ideas and experiences reinforce one another and can be linked to enhance the learning process
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u can use artificial inteligence in data mining the information on organic chemistry.\n\nAlso you can simulate, model and predict the chemical reactions using software based on artificial interlligence
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i thikn this page will help you with all your artificial intelligence questions\n\nhttp://en.wikipedia.org/wiki/Artificial_intelligence
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http://www.microsoft.com/learning/mcp/mcse/\n\nhttp://www.novell.com/training/certinfo/cne/
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That depends on your definition of intelligence.\n\nThis is just the way I see it, machine learning is a machine learning, by training or whatever process, how to execute a process rather than it being preprogrammed. An example of this is the adaptive programming in the engine control computers of cars that have been around for almost 20 years.\n\nArtificial intelligence would, in my opinion, not only would it learn what to do, but also decide when to do what it wants to do. Intelligence, at least partly, is the ability to make decisions without instruction and programming.\n\nI know that this is vague, but I am not sure if that is what you were looking for. Hope this helps.
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bdenton2k is right. Also to use mnemonics.\n\nHere are some links that you may want to refer to:\n\n1) http://www.memletics.com/manual/mnemonics.asp\n2) http://www.fun-with-words.com/mnemonics.html\n3) http://www.coun.uvic.ca/learn/program/hndouts/mnemon.html
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I really recommend reading Tony Buzan's books on mindmapping and learning - very useful and applicable.\n\nA website which you mind find useful (memory techniques, brain teasers) is http://www.braingle.com/\n\nAnd when I'm learning lines for a play, I stick post-its everywhere or sing my lines in the shower. Repetition, repetition, repetition.
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www.learnitalianinonemonth.com
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Multiple Intelligence addresses different ways of thinking-different ways of looking at a problem, or a topic. Some have to see it written down, some have to see a demonstration, others simply have to hear it repeated several times. Of course, some need all 3, or a combination of any 2. Depending on the topic, these combinations can change.\nThis is VERY simplified--but, it's a start.
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Computers are lacking in their capability to learn. They tend to surpass humans in pure rationality issues, and eventually they'll be better at certain things humans are good at intuitively just by taking in enough information. However, the algorithms for a computer's intelligence requires everything to be objective. So there's no way for it to interpret something unless there's an algorithm there to determine how to interpret it. In short, the brain is self-programming. A computer does not really have that capability. To do such would require human beings knowing how to program computers that can program brains. And at that point, we might as well just program our own damned brains to be like that.\n\nBut in terms of raw processing speed, sure, computers will eventually surpass mankind. After all, if nothing else, they're likely using superior technology. All we have is a bunch of neurons and whatnot.
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Your question is not clear. Furnish some examples.
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Artificial intelligence (AI) is defined as intelligence exhibited by an artificial entity. Such a system is generally assumed to be a computer.\n\nAlthough AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behavior, learning and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, speech, and facial recognition. As such, it has become a scientific discipline, focused on providing solutions to real life problems. AI systems are now in routine use in economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games like computer chess and other video games.
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There are different ways of learning for different people. Visual learners need to see things like pictures, auditory learners need to be told by someone. There's a couple more but I'm not sure what their called. I know there's a hands on learner...that's me...I need to do something to learn it, and then there's the people that learn well from books. It just depends on the way you retain information.
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http://www.aaai.org/AITopics/html/engin.html that web site has all you are looking for. Have funn. Or http://cms.mit.edu/games/education/proto.html
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1 Machine language\n2 Assembly language\n3 High level Language
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There really is nothing magical about AI.   It is a form of search and or conditional calculation.\n\nIt is very possible to write AI applications in structured languages.  For instance, I wrote an expert system for a class in grad school (back in the '80s) in Turbo Pascal.  Got an A on it, too.  I'm sure that you can do the same things now in Perl or C# or Java.\n\nMost neural nets, games, & genetic algorithms are probably written in C/C++ and they most likely have portions in assembly for optimization.  These are certainly NOT considered "AI Languages".\n\nThe classical uses for Lisp and Prolog are for expert systems and other decision based systems.  That doesn't mean that they can't be used for other things.  A company I worked for back in the early '90s built and sold CASE tools written in Lisp.  Nobody would think of building a CASE tool in Lisp nowadays, even though a good portion of the DB design tool is in fact a rules based Expert System -- the "perfect" place to use lisp.\n\nI guess my point here is that you shouldn't get wrapped up in the languages themselves.  Each language offers strengths and weaknesses as it is nothing more than a tool to solve the problem.  A well engineered solution is far more important than the language it is written in.