Major Subfields of AI - Machine Learning (ML)

Machine learning (ML) represents an important sub-field of AI where algorithms are utilized to learn when presented with data, and its key sub-types are supervised, unsupervised, semi-supervised and reinforcement learning. Additional essential fields in or connected to the field of ML are deep learning, based on neural networks; self-supervised learning and transfer learning. 

Key subfields of ML

• Supervised Learning: This is where algorithms are trained on labelled data to make a prediction.
• Unsupervised Learning: It is a training process which learns to identify patterns and structures using data which is not labeled.
• Semi-Supervised Learning: Labelled and unlabelled data are combined to acquire an understanding.
• Reinforcement Learning: These algorithms learn on a trial and error basis being rewarded or punished by their actions.
• Deep Learning: A sub-field of ML based on deep neural networks inspired by the human brain to learn based on large datasets.
• Self-Supervised Learning: This is a form of supervised learning, except that the data is also self-labeled.
• Transfer Learning: The model that has been trained on one task is re-used as the initial point of a model on a second task. 

Applications of ML

• Image recognition and speech recognition.
• Predictive analytics
• Recommendation systems
• Natural language processing (NLP)?
• Computer vision