They can be differentiated based on the following category-
Supervised learning: the aim is to approximate the mapping function such that whenever there is a new input data then the corresponding output variable can be predicted.
Unsupervised Learning: aim of is to model the distribution in the data in order to learn more about the data.
2. Input Data
Supervised learning:Uses Known and Labeled Data as input
Unsupervised Learning:Uses unknown and Labeled Data as input
3. Computational Complexity
Supervised learning:Very Complex
Unsupervised Learning:Less Complex
4. Real Time
Supervised learning:Uses off-line analysis
Unsupervised Learning:Uses Real Time Analysis of Data
5. Accuracy of results
Supervised learning: Accurate and Reliable Results
Unsupervised Learning: Moderate Accurate and Reliable Results
If you want to learn Types of Supervised and Unsupervised Learning refer to this.