Preface

Machine learning is about programming computers to optimize a performance
criterion using example data or past experience.

Examples of Machine Learning

  1. Recognition of spoken speech. That is, converting the acoustic speech signal to an ASCrr text.
  2. Consider routing packets over a computer network. The path maximizing the quality of service from a source to destination changes continuously as the network traffic changes. A learning routing program is able to adapt to the best path by monitoring the network traffic.
  3. Intelligent user interface that can adapt to the biometrics of its user, namely, his or her accent, handwriting, working habits, and so forth.
  4. Retail companies analyze their past sales data to learn their customers’ behavior to improve customer relationship management.
  5. Financial institutions analyze past transactions to predict customers’ credit risks.
  6. Robots learn to optimize their behavior to complete a task using minimum resources.
  7. In bioinformatics, the huge amount of data can only be analyzed and knowledge be extracted using computers.
  8. Cars that can drive themselves under different road and weather conditions.
  9. Phones that can translate in real time to and from a foreign language.
  10. Autonomous robots that can navigate in a new environment, for example, on the surface of another planet.

 

The book I’m reading, discusses many methods that have their bases in different fields; statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

 

3 thoughts on “Preface

  1. Thanks ,but some notes:

    1- In bioinformatics, the huge amount of data can only be analyzed and knowledge be extracted using computers.
    – “a real example will be better”

    2- Phones that can translate in real time to and from a foreign language. -“how will they define the language of the speaker ,is that even feasible ?”

  2. 1- Read the example below:
    “Machine learning methods are also used in bioinformatics. DNA in our genome is the “blueprint of life” and is a sequence of bases, namely, A, G, C, and T. RNA is transcribed from DNA, and proteins are translated from the RNA. Proteins are what the living body is and does. Just as a DNA is a sequence of bases, a protein is a sequence of amino acids (as defined by bases). One application area of computer science in molecular biology is alignment, which is matching one sequence to another. This is a difficult string matching problem because strings may be quite long, there are many template strings to match against, and there may be deletions, insertions, and substitutions. Clustering is used in learning motifs, which are sequences of amino acids that occur repeatedly in proteins. Motifs are of interest because they may correspond to structural or functional elements within the sequences they characterize. The analogy is that if the amino acids are letters and proteins are sentences, motifs are like words, namely, a string of letters with a particular meaning occurring frequently in different sentences.”

    Source: Machine Learning an Introduction.

    2- About phones, read original text from book. This is considered as future application (note that book was published in 2004):
    “We can only imagine what future applications can be realized using machine learning: Cars that can drive themselves under different road and weather conditions, phones that can translate in real time to and from a foreign language”.

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