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The Place of Machine Teaching in Machine Learning

Machine learning (ML) – a subset of artificial intelligence (AI) has been around for a while, bringing about the ability where computers are able to tackle tasks that have usually only been carried out by people.

By definition – Machine learning is the ability of algorithms and statistical models of machines or systems to automatically learn and improve from experience without being programmed or without using any explicit instructions or human intervention.

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. This portrays the point that though machine learning allows the machine to learn from experience, there is a place of a “teacher” that teaches the system how to learn from experience – that is where Machine teaching comes in to play.

What is Machine Teaching?

Machine teaching is a discipline that focuses on the development of an optimal training set that allows a learning algorithm to become more efficient. As a result, ML algorithms use the training sets to learn and improve their skills without programming.

Machine teaching is also an approach where human expertise and abilities to find solutions to problems – are adopted in order to help machine learning models find important hints about how to find a solution faster.

 There are 3 basic processes machine teaching teaches machines to learn:

  1. Supervised Learning: During this training, systems are exposed to large amounts of labeled data, with some systems needing to be exposed to millions of examples to master a task.
  2. Unsupervised Learning: in this training, data are not labeled. This algorithm is not designed to single out specific types of data, they simply look for data that can be grouped by similarities, or for anomalies that stand out. This is mostly useful in research and science when looking for hidden patterns.
  3. Semi-Supervised Learning: this teaching approach, uses both the supervised and unsupervised learning processes – using a small amount of labeled data and a large amount of unlabelled data to train systems.

Machine teaching is directly connected to machine learning fundamentals, it emphasizes the teacher’s actions, interactions with data, and design principles of interaction and visualization while taking advantage of human expertise to make AI systems more powerful by telling them what to focus on.