Chapter 2: INTRODUCTION TO MACHINE LEARNING
2.1 Definition
Learning like intelligence, covers a wide range of processes that it is challenging to define accurately.
Regarding machines, we might define, very broadly, that a machine learns whenever it changes its structure, program, or data (based on its inputs or in response to external information) in such a manner that its expected future performance improves [5]. Machine learning is a type of artificial intelligence (AI) that equips computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
The process of machine learning is analogous to data mining. Both systems explore through data to look for patterns. However, machine learning uses that data to detect patterns in data and adjust program actions accordingly. Currently, machine learning is more advanced from pattern recognition to theory that computers can learn without being programmed to perform specific tasks.
The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. [6]
Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire unique skills or adapt to its environment
Definitions of learning vary drastically. This is primarily due to the differing conceptions of what learning actually is. Saljo (1979) identified five categories of learning. It is suggested that the five categories: “…increase in
One of the interesting areas of psychology is learning. From the womb to the last day that you live, the human body is at constant learning mode. As we grow the different stages of life will experience different ways of learning.
Training an artificial Neural Network involves choosing and allowing models for models which there are several associated algorithms.
Learning is an ongoing process; it starts at birth and continues till ones death. New life experiences aid the learning process. Individuals learn something new almost every single day and therefore learning is on of the most essential and important processes. Learning involves acquiring and modifying knowledge, skills, strategies, beliefs, attitudes and behaviors (Schunk, 1996). Learning takes place in many forms, such as cognitive, linguistic, motor and social skills as well. It is a basic concept that must be learnt by every individual in order to be able to function daily.
In order to fully understand our research, we need to understand the basics. What is learning? This is the start point of
Dr. Ahrendt noted the huge advancements that have been made over the last decade, but made sure to note that the math behind AI and machine learning is quite old mathematics. “Now that we can compute things so quickly… we can see the bloom of AI and machine learning.”
At the same time, he gives a broad overview about machine learning, and its potential role in the future. He describes a world where programs
Simple and frank as it could sound, the main features of the upcoming of data knowledge have a
Researchers define learning as a permanent change in performance of individuals undergoing the process of adaptation. Learning is considered as an act of individual to enhance his knowledge. Every person in this world will pay proper attention to what he learns, how he learns, and when he learns. There are specific learning styles for each one of us and it differs from person to person. And, it is due to this reason that researchers have defined nature of learning as paradoxical.
The inductive problem is derived from the mechanisms through which a specific type of knowledge is created. With inductive reasoning the present behavior of objects are used to project future
Complexity Learning Theory describes how situations unfold unpredictably from the components that are in play around them.
Learning is a multifaceted perception unique to each individual. In looking to address the intricacies of learning, there have been a multitude of learning theories established over the centuries. To this day new theories are developed and traditional theories continue to be developed and expanded upon. (Swinburne Online, 2016)
Most of the time we might have to clean and process the data to find some hidden insights in the data. This is called data processing.
Having better, more accurate data opens the way for improved decision support based upon machine learning – as we will see later.