Instance-Based Learning & Model-Based Learning
Machine learning can be divided into two categories based on the learning process and weights management.
Think of the machine learning program you implemented at the beginning of this lesson. Is the weight guess saved somewhere for future use? Can the model work better for a different case later when you guess a different number? No. This is called instance-based learning. In instance-based learning, the algorithm works only for the current instance of data. Instance-based learning is highly dependent on the data and does not work later if you do not provide data.
On the other hand, some machine learning algorithms can save their weights for later use. These kinds of algorithms, called model-based learning, can relearn and improve themselves if more data is provided. When doing predictions, these algorithms do not depend on the data. All deep learning algorithms are model-based learning processes.
Instance-based learning algorithms are usually faster than model-based learning. The Bayesian network model (or k- NN clustering algorithm), for example, is always faster than a deep learning algorithm when learning.
