Why the goals of ML are important and desirable

It is self-evident that the goals of ML are important and desirable. However, we still give some more supporting argument to this issue. First of all, implanting learning ability in computers is practically necessary. Present day computer applications require the representation of huge amount of complex knowledge and data in programs and thus require tremendous amount of work. Our ability to code the computers falls short of the demand for applications. If the computers are endowed with the learning ability, then our burden of coding the machine is eased (or at least reduced). This is particularly true for developing expert systems where the “bottle-neck” is to extract the expert’s knowledge and feed the knowledge to computers. The present day computer programs in general (with the exception of some ML programs) cannot correct their own errors or improve from past mistakes, or learn to perform a new task by analogy to a previously seen task. In contrast, human beings are capable of all the above. Machine Learning Course will produce smarter computers capable of all the above intelligent behavior. Second, the understanding of human learning and its computational aspect is a worthy scientific goal. We human beings have long been fascinated by our capabilities of intelligent behaviors and have been trying to understand the nature of intelligence. It is clear that central to our intelligence is our ability to learn. Thus a thorough understanding of human learning process is crucial to understand human intelligence. ML will gain us the insight into the underlying principles of human learning and that may lead to the discovery of more effective education techniques. It will also contribute to the design of machine learning systems. Finally, it is desirable to explore alternative learning mechanisms in the space of all possible learning methods. There is no reason to believe that the way human being learns is the only possible mechanism of learning. It is worthy exploring other methods of learning which may be more efficient, effective than human learning. We remark that Machine Learning has become feasible in many important applications (and hence the popularity of the field) partly because the recent progress in learning algorithms and theory, the rapidly increase of computational power.

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