How do machines “learn”?
How do ML algorithms solve these “input → output” problems, i.e., how do they recognize patterns and learn rules from data?
The set of ML algorithms can be subdivided according to their learning strategy. This is inspired by how humans learn:
Analogously, machines can also learn by following these three strategies:
 Data requirements for learning according to these strategies:

Unsupervised Learning: a dataset with examples

Supervised Learning: a dataset with labeled examples

Reinforcement Learning: a (simulation) environment that generates data (i.e., reward + new state) in response to the agent’s actions
With its reliance on a datagenerating environment, reinforcement learning is a bit of a special case. Furthermore, as of now it’s still really hard to get reinforcement learning algorithms to work correctly, which means they’re currently mostly used in research and not so much for practical applications.
Supervised Learning
Supervised learning is the most common type of machine learning used in today’s applications.
The goal here is to learn a model (= a mathematical function) \(f(x)\) that describes the relationship between some input(s) \(x\) (e.g., different process conditions like temperature, type of material, etc.) and output \(y\) (e.g., resulting product quality).
This model can then be used to make predictions for new data points, i.e., compute \(f(x') = y\) for some new \(x'\) (e.g., predict for a new set of process conditions whether the produced product will be of high quality or if the process should be stopped to not waste resources).
 Supervised Learning in a nutshell:

Before we start, we need to be very clear on what we want, i.e., what should be predicted, how will predicting this variable help us achieve our overall goals and create value, and how do we measure success, i.e., what is the Key Performance Indicator (KPI) of our process. Then, we need to collect data — and since we’re using supervised learning, this needs to be labeled data, with the labels corresponding to the target variable that we want to predict. Next, we “learn” (or “train” or “fit”) a model on this data and finally use it to generate predictions for new data points.
Video Recommendation: If you’re not familiar with linear regression, the most basic supervised learning algorithm, please watch the explanation from Google decision scientist Cassie Kozyrkov on how linear regression works: [Part 1] [Part 2] [Part 3] 
The available supervised learning algorithms differ in the type of \(x \to y\) relationship they can describe (e.g., linear or nonlinear) and what kind of objective they minimize (also called loss function; an error computed on the training data, quantifying the mismatch between true and predicted labels). The task of a data scientist is to select a type of model that can optimally fit the given data. The rest is then taken care of by an optimization method, which finds the parameters of the model that minimize the model’s objective, i.e., such that the model’s prediction error on the given data is as small as possible.
In most of the book, the terms “ML algorithm” and “ML model” will be used interchangeably. To be more precise, however, in general the algorithm processes the data and learns some parameter values. These parameter settings define the final model. For example, a linear regression model is defined by its coefficients (i.e., the model’s parameters), which are found by executing the steps outlined in the linear regression algorithm, which includes solving an optimization problem. 