Artificial Intelligence (AI) - The simulation of human intelligence in machines that are programmed to think and learn like humans.
Machine learning - A subfield of AI that allows systems to learn and improve from data without being explicitly programmed.
Deep learning - A subfield of machine learning that uses neural networks with multiple layers to learn and make predictions.
Neural network - A type of machine learning model that is inspired by the structure and function of the human brain and is used to process and analyze large amounts of data.
Convolutional neural network (CNN) - A type of neural network that is particularly well-suited to image and video analysis.
Recurrent neural network (RNN) - A type of neural network that is used to process sequential data, such as text or speech.
Natural Language Processing (NLP) - A subfield of AI that focuses on the interactions between computers and human languages, such as speech recognition, language translation and text generation.
Robotics - A branch of AI that deals with the design, construction, operation, and use of robots.
Computer vision - A subfield of AI that deals with the ability of machines to interpret and understand visual information from the world.
Reinforcement learning - A type of machine learning where an agent learns to make decisions by receiving feedback in the form of rewards or penalties.
Evolutionary algorithms - A family of algorithms that mimic the process of natural selection to find solutions to optimization problems.
Genetic algorithm - A specific type of evolutionary algorithm that uses the principles of genetics to generate new solutions.
Expert systems - A type of AI that simulates the decision-making abilities of a human expert in a specific domain.
Fuzzy logic - A type of AI that deals with reasoning under uncertainty and is used to model systems with imprecise or vague information.
Knowledge representation - The way in which AI systems store and organize information for use in decision-making and problem-solving.
Symbolic AI - An approach to AI that uses symbolic logic and rule-based systems to represent knowledge and make decisions.
Sub-symbolic AI - An approach to AI that uses numerical methods and techniques such as neural networks to represent knowledge and make decisions.
Supervised learning - A type of machine learning where an algorithm is trained on a labeled dataset with known outputs.