Supervised Learning vs Unsupervised Learning: What’s the Deal?
If you are familiar with machine learning, then you are probably aware of how it has helped pave the way for a lot of the progress made in the field of artificial intelligence. Machine learning has shifted the focus from creating systems that are already smart to creating systems that can learn. With machine learning, systems are not explicitly programmed. Instead, algorithms are created to learn from data and processes.
There are two basic types of machine learning – supervised learning and unsupervised learning.
Supervised machine learning
In supervised machine learning algorithms, both input and output variables are known and the function between the two is learned by the system using the machine learning algorithm. You can think of the system as someone being guided by a teacher. The teacher provides an input and its corresponding desired output; the system predicts the process that is performed on the input to get the desired output. This prediction is used and adjusted as more sets of input and output are provided.
This type of machine learning is being applied in pattern recognition, speech recognition, spam detection, information extraction, handwriting recognition, database marketing, bioinformatics, and cheminformatics, among others.
Unsupervised machine learning
In unsupervised machine learning algorithms, on the other hand, only the input data is provided to the system. The goal of the machine is not to predict a relationship or function between an input and an output, but to learn more about the data itself by analyzing the structure and distribution of the data. There is no guide in unsupervised learning and there are no target outputs for each input. The only goal is to learn what the data is all about.
This type of machine learning is being applied in anomaly detection, cluster analysis, and pattern recognition, among others.
Semi-supervised machine learning
No one said that any particular system has to be strictly supervised or unsupervised. Hence, a lot of computer systems actually a more realistic combination of the two, because many real-world machine learning problems also cannot be strictly categorized into either of the two. Many are problems that require both analysis of the input to better understand it and predicting the input-output relationship of the system. Most intelligent automation products like WorkFusion’s Smart Process Automation makes use of both supervised and unsupervised machine learning.
Author Bio: Rana Tarakji is an entrepreneur and a contributing writer at OneSEO (http://oneseo.net/blog) who lives in Beirut, Lebanon. Rana Tarakji is passionate about digital marketing, startups, helping entrepreneurs grow, and empowering them to live their dreams. She has worked as a digital marketer, a technology co-founder and business developer, and a writer.