要解决的主要问题以及为这篇文章提供标题的一点是，机器学习不仅仅是荣耀的统计数据 – 与旧计算机一样，只需要更大的计算机和更出色的名字。这个概念来源于机器学习中普遍存在的统计概念和术语，如回归，权重，偏差，模型等。此外，许多模型近似认为统计函数：分类模型的softmax输出包括logits，使得训练图像分类器的过程成为逻辑回归。虽然这种思路在技术上是正确的，但将整个机器学习仅仅归结为统计学的附属部分是相当长的。事实上，比较没有多大意义。统计学是数学领域，涉及数据的理解和解释。机器学习不过是一类计算算法（因此它出现在计算机科学领域）。在许多情况下，这些算法在帮助理解数据方面完全没有用处，并且仅在某些类型的不可预知的预测建模中起作用。在某些情况下，例如在强化学习中，算法可能根本不使用预先存在的数据集。另外，在图像处理的情况下，将图像作为以像素为特征的数据集的实例作为开始的一段延伸。
The main point to address, and the one that provides the title for this post, is that machine learning is not just glorified statistics—the same-old stuff, just with bigger computers and a fancier name. This notion comes from statistical concepts and terms which are prevalent in machine learning such as regression, weights, biases, models, etc. Additionally, many models approximate what can generally be considered statistical functions: the softmax output of an classification model consists of logits, making the process of training an image classifier a logistic regression. Though this line of thinking is technically correct, reducing machine learning as a whole to nothing more than a subsidiary of statistics is quite a stretch. In fact, the comparison doesn’t make much sense. Statistics is the field of mathematics which deals with the understanding and interpretation of data. Machine Learning is nothing more than a class of computational algorithms (hence its emergence from Computer Science). In many cases, these algorithms are completely useless in aiding with the understanding of data, and assist only in certain types of uninterpretable predictive modeling. In some cases, such as in reinforcement learning, the algorithm may not use a pre-existing dataset at all. Plus, in the case of image processing, referring to images as instances of a dataset with pixels as features was a bit of a stretch to begin with.