The 3 Basic Paradigms of Machine Learning
Modern machine learning is arguably the intermediate step between heuristics and artificial intelligence (AI). While AI will be a form of machine learning, the current generation of ideas we use will probably no longer be relevant when a breakthrough happens which allows for true artificial intelligence. Machine learning transcends traditional algorithms and rules when done right because it adapts to patterns which are harder to quantify and qualify. You don’t just map out rules, you show the system what you want, have, or let it experience the system and it seemingly learns from it.
Modern day machine learning isn’t magic (despite what SaaS companies may say), and ironically it isn’t even all that intelligent. It’s basically fancy math to turn a system into a process. You can either react to the inputs ( unsupervised learning), the inputs and the outputs ( supervised learning), or the environment ( reinforcement learning) with the primary paradigms. As these are neither inclusive nor exclusive, they can be combined to get more complicated systems and processes.
The model can affect how effective the learning is, but the paradigm is going to affect the process. A specific task is different than trying to make sense of data. An environment is different than fixed inputs or outputs. Let’s go over what each paradigm is and what it means for machine…
