Machine Learning
Machine Learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Because of new computing technologies, machine learning today is not like machine learning of the past. Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks.
Machine learning is a method for achieving artificial intelligence. Since its birth Artificial Intelligence was defined as any machine capable of performing a task that would typically require human intelligence. Artificial Intelligence systems will demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and social intelligence and creativity. Alongside machine learning there are different other approaches used to build Artificial Intelligence systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to evolve optimal solutions and expert systems.
Now let’s study some popular methods of machine learning: supervised, unsupervised, semisupervised and reinforcement learning.
Supervised learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. Supervised learning is commonly used in applications where historical data predicts likely future events.
Unsupervised learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.
Semisupervised learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data. This type of learning can be used with methods such as classification, regression and prediction.
Reinforcement is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.
Now you have an imagination about machine learning, we hope it was useful and interesting for you. Thank you for your time and consideration.
Sincerely Yours,
TCO team
