Tapping into the “folk knowledge” needed to advance machine learning applications

Tapping into the “folk knowledge” needed to 

advance machine learning applications

Pedro Domingos

Howdy!

             I am going to briefly summarize and express my opinion on the above mentioned article.

             In this article, the author talks about Machine Learning and gives a simplified overview of the field. He introduces the topic of machine learning, talks about different ways it can be achieved and discusses various related issues.

              Machine Learning algorithms automatically figure extract knowledge from the input data. It learns different rules to distinguish between the different data and acts as a classifier when new data is presented to it. Learning in machine learning algorithms is a combination of representation, evaluation and optimization. The classifier has to be represented in a formal language that the computer can handle. This decided what kind of hypotheses the learner can learn, The set of hypotheses that the learner can learn is called hypothesis space. An evaluation function or an objective function is used to distinguish between good and bad classifiers. The optimization process is used to find the classifier with the optimal efficiency. 

             Generalization is the main goal of machine learning algorithms. The "goodness" of a machine learning algorithm depends on how well the classifier obtained can classify new input instances. Using a lot of data or a lot of features can induce noise and result in a bad classifier. When the example data cannot cover all the different variety in the total data, it may produce a bad classifier. This problem is known as overfitting. Bias is a learner's tendency to learn the same wrong thing consistently. Variance is the tendency of the learner to learn random things irrespective of the correct signal. 

Figure 1

Our intuition that works pretty well in a 3- D world may not work at all in high dimensions. Sometimes, data fed to a machine learner has a lot of dimensions and so it is hard to design algorithms for machine learners. There are some theoretical guarantees about machine learning algorithms but they are probabilistic guarantees. A lot of feature engineering goes into machine learning projects and it cannot be automated. So the actual learning part may take a very short time compared to the time that goes into the preprocessing stages. Recently, the focus has shifted from using just one machine learning algorithm to many. Different algorithms are weighted differently and the results are combined based on those weights to get the final output. This results in a very good classifier.

The knowledge presented in the paper about machine learning is very useful. It explains the basics of machine learning in a very lucid manner. Machine learning seems to be the next big thing in the world of computing. 

I used the following sources for this blog post:

[1] Magazine. Communications of the ACM CACM Homepage archive. Volume 55 Issue 10, October 2012. Pages 78-87. ACM New York, NY, USA

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