"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
It studies how machines can learn and adapt to complex environments and situations.
Machine learning: Algorithms and techniques that enable machines to learn from data, identify patterns, and make predictions without being explicitly programmed.
Deep learning: A subset of machine learning that involves training neural networks to learn and recognize patterns and features in large datasets.
Natural language processing: Techniques that enable computers to understand and process human language, including speech recognition, text analysis, and machine translation.
Computer vision: The science of teaching computers to interpret and understand visual data, including image and video recognition, object detection, and facial recognition.
Robotics: The study and design of robots that can execute tasks autonomously, with or without human supervision.
Cognitive computing: A field of AI that aims to create systems that can reason, learn, and perceive like humans, using techniques such as machine learning, natural language processing, and computer vision.
Reinforcement learning: A type of machine learning that involves training an AI system to learn by receiving feedback and rewards for making certain decisions or taking certain actions.
Swarm intelligence: A collective behavior of decentralized, self-organized systems that are able to solve complex problems through interactions among large numbers of individual agents.
Genetic algorithms: Computational techniques that mimic the process of natural selection in order to optimize solutions to complex problems.
Expert systems: AI systems that use rules and knowledge bases to reason and make decisions in complex domains, such as medicine or finance.
Bayesian networks: Probabilistic models that can be used to make predictions or decisions based on uncertain or incomplete information.
Fuzzy logic: A mathematical framework that can represent and reason with uncertain or vague concepts and relationships.
Evolutionary computing: A family of optimization algorithms inspired by biological evolution, including genetic algorithms, evolutionary strategies, and evolutionary programming.
Neural networks: Mathematical models that simulate the behavior of neurons in the brain and can be used for pattern recognition, prediction, and classification.
Decision trees: Hierarchical models that represent a sequence of decisions and their consequences, often used in classification and regression tasks.
Rule-based AI: Rule-based systems use a set of if-then rules to make decisions. These types of AI are useful for solving narrow, well-defined problems.
Expert Systems: Expert systems are computer programs that mimic the decision-making abilities of a human expert in a particular field. These systems use knowledge and inference rules to provide recommendations or solve problems.
Fuzzy Logic: Fuzzy logic is a type of artificial intelligence that deals with ambiguity and uncertainty. It is used to make decisions in systems where the criteria may not be precise or where there are multiple possible outcomes.
Neural Networks: Neural networks are a type of machine learning that use interconnected nodes to simulate the way the human brain works. These systems are used for pattern recognition, prediction, and decision-making.
Machine Learning: Machine learning is a type of AI that allows computers to learn and improve over time without being explicitly programmed to do so. It is used for tasks such as image recognition, language translation, and speech recognition.
Deep Learning: Deep learning is a subfield of machine learning that uses deep neural networks to model complex patterns in large data sets. It is used for tasks such as image and speech recognition, natural language processing, and autonomous vehicles.
Natural Language Processing: Natural language processing is a type of AI that allows computers to understand and interpret human language. It is used for tasks such as chatbots, voice assistants, and language translation.
Robotics: Robotics is a type of AI that involves the design and construction of physical robots that can perform tasks autonomously or with human guidance. It is used in industries such as manufacturing, logistics, and healthcare.
Evolutionary Algorithms: Evolutionary algorithms are a type of AI inspired by biological evolution. They are used for tasks such as optimization, scheduling, and decision-making.
Swarm Intelligence: Swarm intelligence is a type of AI inspired by the behavior of social animals such as ants and bees. It is used for tasks such as optimization, routing, and decision-making.
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"Recently, generative artificial neural networks have been able to surpass results of many previous approaches."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"where it is too costly to develop algorithms to perform the needed tasks."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"ML is known in its application across business problems under the name predictive analytics."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."
"the problems are solved by helping machines 'discover' their 'own' algorithms without needing to be explicitly told what to do by any human-developed algorithms."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"development of algorithms by human programmers would be cost-prohibitive"
"generative artificial neural networks have been able to surpass results of many previous approaches."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"Machine-learning approaches have been applied to...medicine."
"helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"where it is too costly to develop algorithms to perform the needed tasks."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."