The Gap: Where Machine Learning Education Falls Short

The Gradient:

As the field of machine learning has become ever more popular, a litany of online courses has emerged claiming to teach the skills necessary to “build a career in AI”. But before signing up for such a course, you should know whether the skills acquired will directly allow you to apply machine learning better. These questions are not limited to online courses but rather encompass machine learning classes that have begun to fill lecture halls at many universities. Are these classes that students flock towards actually helping them achieve their practical goals?

The Current State of Machine Learning Education

Having taken the main slate of the seminal machine learning courses at one of the top universities for AI, I have found a general guideline most classes follow. First, they tend to start with linear classifiers and introduce the concepts of both regression and classification along with the concepts of loss functions and optimization. Afterward, a week or two is spent on honing the skill of backpropagation after which they dive into neural networks fully. If the course focuses on deep learning, it tends to spend the majority of the remaining time diving extensively into the different forms of neural networks (RNN, LSTMs, CNNs, etc) and about recently published seminal architectures (ResNet, BERT, etc). If the course instead focuses on more general machine learning principles, it introduces other avenues such as unsupervised and reinforcement learning.

Thus we see that the key topics covered in these courses can be distilled into the following: an overview of supervised learning, a brief introduction to the mathematical foundations underlying supervised learning and neural networks, and then either an introduction to deep learning methodologies or to other areas of machine learning.