Machine Learning

Inteligencia Artificial

Machine Learning

• Definition: A subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.
• Types:
  • Supervised Learning: Algorithm learns from labeled data to make predictions.
  • Unsupervised Learning: Algorithm discovers patterns in unlabeled data.
  • Reinforcement Learning: Algorithm learns through trial and error by receiving rewards or penalties.
• Applications: Image and speech recognition, natural language processing, recommender systems.

Computer Vision

• Definition: A field of study that focuses on enabling computers to interpret and understand visual information from the world.
• Applications:
  • Image classification and object detection.
  • Image segmentation and tracking.
  • Scene understanding and 3D reconstruction.
• Techniques:
  • Convolutional Neural Networks (CNNs) for image processing.
  • Object Detection Architectures (YOLO, SSD, Faster R-CNN).

Deep Learning

• Definition: A subset of Machine Learning that involves the use of artificial neural networks with multiple layers to learn complex patterns in data.
• Types:
  • Convolutional Neural Networks (CNNs) for image and signal processing.
  • Recurrent Neural Networks (RNNs) for sequential data processing.
  • Generative Adversarial Networks (GANs) for generating new data.
• Applications: Image and speech recognition, natural language processing, game playing.

Neural Networks

• Definition: A model inspired by the structure and function of the human brain, composed of interconnected nodes (neurons) that process and transmit information.
• Components:
  • Input Layer: Receives input data.
  • Hidden Layers: Process and transform input data.
  • Output Layer: Produces the final output.
• Training: Backpropagation algorithm is used to optimize the neural network's performance.

Natural Language Processing (NLP)

• Definition: A field of study focused on the interaction between computers and human language.
• Applications:
  • Text classification and sentiment analysis.
  • Language translation and language generation.
  • Speech recognition and dialogue systems.
• Techniques:
  • Tokenization and stopword removal.
  • Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
  • Word embeddings (Word2Vec, GloVe).

Inteligencia Artificial

Machine Learning

• Machine learning involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.
• There are three types of machine learning: supervised, unsupervised, and reinforcement learning.
  • Supervised learning involves learning from labeled data to make predictions.
  • Unsupervised learning involves discovering patterns in unlabeled data.
  • Reinforcement learning involves learning through trial and error by receiving rewards or penalties.
• Machine learning has various applications, including image and speech recognition, natural language processing, and recommender systems.

Computer Vision

• Computer vision is a field of study that focuses on enabling computers to interpret and understand visual information from the world.
• Applications of computer vision include image classification and object detection, image segmentation and tracking, and scene understanding and 3D reconstruction.
• Techniques used in computer vision include convolutional neural networks (CNNs) for image processing and object detection architectures such as YOLO, SSD, and Faster R-CNN.

Deep Learning

• Deep learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers to learn complex patterns in data.
• There are three types of deep learning: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
• Applications of deep learning include image and speech recognition, natural language processing, and game playing.

Neural Networks

• A neural network is a model inspired by the structure and function of the human brain, composed of interconnected nodes (neurons) that process and transmit information.
• A neural network consists of an input layer, hidden layers, and an output layer.
• The backpropagation algorithm is used to optimize the neural network's performance during training.

Natural Language Processing (NLP)

• Natural language processing is a field of study focused on the interaction between computers and human language.
• Applications of NLP include text classification and sentiment analysis, language translation and language generation, and speech recognition and dialogue systems.
• Techniques used in NLP include tokenization and stopword removal, named entity recognition (NER) and part-of-speech (POS) tagging, and word embeddings such as Word2Vec and GloVe.