Conceptual Map International Standar

Conceptual Map International Standar

1. machine learning

   • What is?
     • is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.
   • Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers.
   • Types of problems and tasks
     • Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are
       • Supervised learning
       • Unsupervised learning
       • Reinforcement learning

2. deep learning

   • What is?
     • is the use of artificial neural networks that contain more than one hidden layer. Applications include pattern analysis and classification.
       • Deep learning is part of a broader family of machine learning methods based on learning data representations.
   • Definitions
     • use a cascade of many layers of nonlinear processing units for feature extraction and transformation.

3. soft computing

   • What is?
     • Is a branch of Artificial Intelligence that encompasses various techniques used to solve problems that handle incomplete, uncertain and / or inaccurate information.
       • Soft computing techniques include:
         • Neural networks Fuzzy Systems Bio-inspired computing: Evolutionary Algorithms Optimization of ants colonies Swarm intelligence Ideas about probability: Bayesian networks Chaos theory

4. Convulsions nerworks

   • What is ?
     • In machine learning, a convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex.
       • The convolutional neural network is also known as shift invariant or space invariant artificial neural network (SIANN), which is named based on its shared weights architecture and translation invariance characteristics
         • Image recognition
           • Convolutional neural networks (CNNs) consist of multiple layers of receptive fields.
         • Neural abstraction pyramid
           • The feed-forward architecture of convolutional neural networks was extended in the neural abstraction pyramid[27] by lateral and feedback connections.