It should also be acknowledged that many machine learning algorithms require a stronger background in statistics and probability than do most neural network techniques, but even these approaches are often referred to as statistical machine learning or statistical learning , as if to distinguish themselves from the regular, less statistical kind. Furthermore, most of the hype-fueling innovation in machine learning in recent years has been in the domain of neural networks, so the point is irrelevant.
No, Machine Learning is not just glorified Statistics
Again, in the real world, anyone hoping to do cool machine learning stuff is probably working on data problems of a variety of types, and therefore needs to have a strong understanding of statistics as well. To be fair to myself and my classmates, we all had a strong foundation in algorithms, computational complexity, optimization approaches, calculus, linear algebra, and even some probability.
All of these, I would argue, are more relevant to the problems we were tackling than knowledge of advanced statistics. Pedro Domingos, a professor of computer science at the University of Washington, laid out three components that make up a machine learning algorithm: representation, evaluation, and optimization. Representation involves the transformation of inputs from one space to another more useful space which can be more easily interpreted.
Think of this in the context of a Convolutional Neural Network. Raw pixels are not useful for distinguishing a dog from a cat, so we transform them to a more useful representation e. Evaluation is essentially the loss function. How effectively did your algorithm transform your data to a more useful space? How closely did your softmax output resemble your one-hot encoded labels classification?
Did you correctly predict the next word in the unrolled text sequence text RNN?
Mind: How to Build a Neural Network (Part One)
How far did your latent distribution diverge from a unit Gaussian VAE? These questions tell you how well your representation function is working; more importantly, they define what it will learn to do. Optimization is the last piece of the puzzle. Once you have the evaluation component, you can optimize the representation function in order to improve your evaluation metric.
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In neural networks, this usually means using some variant of stochastic gradient descent to update the weights and biases of your network according to some defined loss function. And voila! Borrowing statistical terms like logistic regression do give us useful vocabulary to discuss our model space, but they do not redefine them from problems of optimization to problems of data understanding. Aside: The term artificial intelligence is stupid. In the 19th century, a mechanical calculator was considered intelligent link.
I wish we could stop using such an empty, sensationalized term to refer to real technological techniques. Further defying the purported statistical nature of deep learning is, well, almost all of the internal workings of deep neural networks. Fully connected nodes consist of weights and biases, sure, but what about convolutional layers?
Rectifier activations? Batch normalization? Residual layers? Memory and attention mechanisms? Let me also point out the difference between deep nets and traditional statistical models by their scale. Deep neural networks are huge. How do you think your average academic advisor would respond to a student wanting to perform a multiple regression of over million variables?
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The idea is ludicrous. I will remind you, however, that not only is deep learning more than previous techniques, it has enabled to us address an entirely new class of problems. Prior to , problems involving unstructured and semi-structured data were challenging, at best. This has yielded considerable progress in fields such as computer vision, natural language processing, speech transcription, and has enabled huge improvement in technologies like face recognition, autonomous vehicles, and conversational AI.
That said, it has made a significant contribution to our ability to attack problems with complex unstructured data. Many have interpreted this article as a diss on the field of statistics, or as a betrayal of my own superficial understanding of machine learning. In retrospect, I regret directing so much attention on the differences in the ML vs. Let me be clear: statistics and machine learning are not unrelated by any stretch. Machine learning absolutely utilizes and builds on concepts in statistics, and statisticians rightly make use of machine learning techniques in their work.
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The distinction between the two fields is unimportant, and something I should not have focused so heavily on. Recently, I have been focusing on the idea of Bayesian neural networks. Sometimes the analysis of results indicates that the predictive ability of the data is limited, thus suggesting the need for new and different data elements during the collection of the next set of data.
For example, results from a new clinical test may be needed. As each database is analyzed, neural networks and statistical analysis can demonstrate the extent to which disease states and outcomes can be predicted from factors in the current database. The accuracy and performance of these predictions can be measured and, if limited, can stimulate the expansion of data collection to include new factors and expanded patient populations.
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Databases have been established in the majority of major medical institutions. These databases originally were intended to provide data storage and retrieval for clinical personnel. However, there now is an additional goal: to provide information suitable for analysis and medical decision support by neural networks and multifactorial statistical analysis. Comparisons of computerized multivariate analysis with human expert opinions have been performed in some studies, and some published comparisons identify areas in which neural network diagnostic capabilities appear to exceed that of the experts.
Traditionally, expert opinions have been developed from the expert's practical clinical experience and mastery of the published literature. Currently we can, in addition, employ neural networks and multivariate analysis to analyze the multitude of relevant factors simultaneously and to learn the trends in the data that occur over a population of patients. The neural network results then can be used by the clinician.
Today, each physician treats a particular selection of patients. Because a particular type of patient may or may not visit a particular physician, the physician's clinical experience becomes limited to a particular subset of patients. A physician then could have access to neural networks trained on a population of patients that is much larger than the subset of patients the physician sees in his or her practice.
When a neural network is trained on a compendium of data, it builds a predictive model based on that data.
The model reflects a minimization in error when the network's prediction its output is compared with a known or expected outcome. For example, a neural network could be established to predict prostate biopsy study outcomes based on factors such as prostate specific antigen PSA , free PSA, complex PSA, age, etc.
The network then would be trained, validated, and verified with existing data for which the biopsy outcomes are known. Performance measurements would be taken to report the neural network's level of success. These measurements could include the mean squared error MSE , the full range of sensitivity and specificity values i. The trained neural network then can be used to classify each new individual patient. The predicted classification could be used to support the clinical decision to perform biopsy or support the decision to not conduct a biopsy.
This is a qualitatively different approach a paradigm shift compared with previous methods, whereby statistics concerning given patient populations and subpopulations are computed and published and a new individual patient then is referenced to the closest matching patient population for clinical decision support. With this new multivariate approach, we are ushering in a new era in medical decision support, whereby neural networks and multifactorial analysis have the potential to produce a meaningful prediction that is unique to each patient.
Neural network for predicting the outcome of a prostate biopsy study. PSA: prostate specific antigen. Artificial neural networks are inspired by models of living neurons and networks of living neurons. Artificial neurons are nodes in an artificial neural network, and these nodes are processing units that perform a nonlinear summing function, as illustrated in Figure 5.
Synaptic strengths translate into weighting factors along the interconnections. Illustration of an artificial neural network processing unit. Each unit is a nonlinear summing node.