Computer Vision and Machine learning

Computer Vision is sub field of the artificial intelligence (AI) , in the Computer Vision we are mainly interested with the Deep learning or Artificial Neural Networks (ANNs) and both of them are sub field of the machine learning. 

In general Machine Learning is concept of developing Model which can take the correct actions based on the learning from the historical and current data instated of taken the actions based on the pre defined logic as we can see in other programming languages, learning form the data is the key part of the Machine Learning, there are three types of the Machine Learning algorithms :

1. Supervised learning : the algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.
2. Unsupervised learning: the algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized.
3. Semi-supervised learning: falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy

Artificial Neural Networks (ANNs) are a class of machine learning algorithms that learn from data and specialize in pattern recognition, inspired by the structure and function of the brain. The deep learning is part of  the of ANN algorithms, and in most cases, the two terms can be used interchangeably.