What is Machine Learning?
A useful way to introduce the machine learning methodology is by means of a comparison with the conventional engineering design flow.
This starts with an in-depth analysis of the problem domain, which culminates with the definition of a mathematical model. The mathematical model is meant to capture the key features of the problem under study and is typically the result of the work of a number of experts. The mathematical model is finally leveraged to derive hand-crafted solutions to the problem.
For instance, consider the problem of defining a chemical process to produce a given molecule. The conventional flow requires chemists to leverage their knowledge of models that predict the outcome of individual chemical reactions, in order to craft a sequence of suitable steps that synthesize the desired molecule. Another example is the design of speech translation or image/ video compression algorithms. Both of these tasks involve the definition of models and algorithms by teams of experts, such as linguists, psychologists, and signal processing practitioners, not infrequently during the course of long standardization meetings.
The engineering design flow outlined above may be too costly and inefficient for problems in which faster or less expensive solutions are desirable. The machine learning alternative is to collect large data sets, e.g., of labeled speech, images, or videos, and to use this information to train general-purpose learning machines to carry out the desired task. While the standard engineering flow relies on domain knowledge and on design optimized for the problem at hand, machine learning lets large amounts of data dictate algorithms and solutions. To this end, rather than requiring a precise model of the set-up understudy, machine learning requires the specification of an objective, of a model to be trained, and of an optimization technique.
Returning to the first example above, a machine learning approach would proceed by training a general-purpose machine to predict the outcome of known chemical reactions based on a large data set, and by then using the trained algorithm to explore ways to produce more complex molecules. In a similar manner, large data sets of images or videos would be used to train a general-purpose algorithm with the aim of obtaining compressed representations from which the original input can be recovered with some distortion.
When to Use Machine Learning?
Based on the discussion above, machine learning can offer an efficient alternative to the conventional engineering flow when development cost and time are the main concerns, or when the problem appears to be too complex to be studied in its full generality. On the flip side, the approach has the key disadvantages of providing generally suboptimal performance, or hindering interpretability of the solution, and applying only to a limited set of problems. In order to identify tasks for which machine learning methods may be useful, suggests the following criteria:
1. the task involves a function that maps well-defined inputs to well-defined outputs;
2. large data sets exist or can be created containing input-output pairs;
3. the task provides clear feedback with clearly definable goals and metrics;
4. the task does not involve long chains of logic or reasoning that depend on diverse background knowledge or common sense;
5. the task does not require detailed explanations for how the decision was made;
6. the task has a tolerance for error and no need for provably correct or optimal solutions;
7. the phenomenon or function being learned should not change rapidly over time; and
8. no specialized dexterity, physical skills, or mobility is required.
These criteria are useful guidelines for the decision of whether the machine learning methods are suitable for a given task of interest. They also offer a convenient demarcation line between machine learning as is intended today, with its focus on training and computational statistics tools, and more general notions of Artificial Intelligence (AI) based on knowledge and common sense.
In short, Machine learning is very useful and so progressive in the field of programming and topics related to computers.
