There are many ways to classify ML algorithms. Here, we’ll use one of them, the types of learning:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
The following bubble chart shows some commons fields of applications for each paradigm.
In this category, the models have as input a set of labeled data. When the labels are discrete, we say that is a classification problem (for example, medical diagnosis, credit analysis, or our previous example on email classification), on the other hand, when labels are continuous, we say a regression problem (wind speed prediction, houses prices based in features). Here, the algorithms try to calculate an explicit function that maps the labeled data with the correspondent input. The supervised algorithms fit the model by error minimization or accuracy maximization. At the end of the training process, the knowledge is stored in the model itself, that is, in the first place, a graph of operators and coefficients or a set of rules.
The most used supervised algorithms are:
- Linear Regression
- Logistic Regression
- Artificial Neural Networks
- Support Vector Machines
- K-Nearest Neighbors
- Decision Trees
- Naive Bayes
Mainly applied for clustering, anomaly detection and association problems, the unsupervised learning algorithms don’t need labeled data. Here, the learning is inherent to the algorithm’s capability in distinguishing the input information by means of distances or pattern recognition. Therefore, the knowledge here is somehow in the data itself, but also as the models’ hyperparameters.
The most used supervised algorithms are:
- K-Means
- Gaussian Mixture
- Hidden Markov Models
- Principal Component Analysis
- Isolation Forest
- DBScan
This paradigm is quite different from the two above. Basically, here the agent (system) is subjected to situations and gets rewards or penalties according to the actions it takes. The most illustrative example is that techniques of reinforcement learning are used to make robots learn how to walk. RL algorithms mixed with deep learning have shown awesome results as well. Here, the knowledge is dynamic, constantly modified.
Thank you for reading.
Special thanks to Gabriel Teotonio (ghft@cin.ufpe.br) and Jair Sales (jps4@cin.ufpe.br).