Machine Learning (ML) and Artificial Intelligence (AI)

What's the Connection Between Machine Learning and Artificial Intelligence

Machine Learning (ML) arose from the search for artificial intelligence as a scientific pursuit. Some academics sought machines to learn from data in the early days of artificial intelligence (AI) as an academic discipline. They explored different symbolic methods and later "neural networks" to solve the problem; these were largely perceptrons and other models that were later discovered as reinventions of generalized linear statistical models. Probabilistic reasoning has also been employed in the medical field, especially in automatic diagnosis.

What's the Difference Between Machine Learning and Artificial Intelligence?

Machine Learning is the process of designing and creating algorithms based on behavior based on experimental data. Artificial intelligence includes issues such as information presentation, natural language processing, planning, and robotics, in addition to machine learning.

A shift between artificial intelligence and machine learning has occurred as the emphasis on logical, knowledge-based approaches has grown. Theoretical and practical issues with data collecting and representation plagued probabilistic systems. Expert systems had taken over artificial intelligence by 1980, and statistics had vanished. Artificial intelligence research into learning based on symbolic knowledge continued, leading to inductive logic programming.

Outside of the field of artificial intelligence, statistical research was now being used in pattern detection and knowledge acquisition. Artificial intelligence and computer science have also abandoned neural network research. Researchers from other disciplines, such as Hopfield, Rumelhart, and Hinton, have continued this trend as "connectionism" outside of the field of AI. The rediscovery of backpropagation in the mid-1980s was the key to their success.

Machine learning flourished in the 1990s after being reorganized as a separate field. The goal of the field has shifted from obtaining artificial intelligence to dealing with real problems that can be solved. He switched the focus away from the symbolic approaches he inherited from AI and toward statistics and probability theory methodologies and models. Many sources indicate that machine learning is a subfield of Artificial Intelligence. Nonetheless, other experts, such as Dr. Daniel Hulme, who teaches AI and leads a company in the subject, contend that machine learning and AI are two different things.

The Connection Between Machine Learning and Data Mining:

Machine learning and data mining both employ similar approaches and have a lot of overlap, but machine learning focuses on learning predictions from data, whereas data mining focuses on finding (pre) unknown features in data (the analysis step of knowledge discovery in these databases). Data mining employs a variety of machine learning techniques, each with its own set of goals; nonetheless, machine learning employs data control techniques such as "unsupervised learning" or a preprocessing step to increase learning accuracy. Machine learning is usually concerned with reproducing known knowledge, whereas knowledge discovery and data mining are concerned with discovering previously undiscovered knowledge. When compared to known data, an uninformed (unsupervised) method readily outperforms supervised methods, whereas supervised methods cannot be employed in a typical data mining assignment due to the lack of learning data.

The Link Between Machine Learning and Optimization:

Machine learning and optimization are inextricably linked. Many learning issues are expressed as loss function minimization in a learning set sample. The discrepancy between the model's predictions and the real-world problem cases is referred to as a loss function (for example, in classification, one wants to assign a label to the samples, and the models are trained to correctly predict the pre-assigned labels of a set of samples). The distinction between the two fields arises from the goal of generalization: optimization algorithms are concerned with minimizing loss in a learning set, whereas machine learning is concerned with minimizing loss in unknown samples. In machine learning, optimization algorithms are crucial for assessing the sensitivity of parameters.

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