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Temario
UNIT 1. MACHINE LEARNING
- Machine Learning
- Types of machine learning
- - Supervised
- - Unsupervised
- - Reinforced
- Machine learning algorithms and models
- Evaluation metrics in machine learning
- Regularization and feature selection in machine learning
UNIT 2. ARTIFICIAL NEURAL NETWORKS (ANN)
- Artificial Neural Networks (ANN)
- Structure and architecture
- Activation functions
- Training of the ANNs
- Convolutional Neural Networks (CNN) and their application
- Recurrent Neural Networks (RNN) and their application
- Generative Adversarial Models (GAN) and their application
UNIT 3. NATURAL LANGUAGE PROCESSING (NLP)
- Fundamentals of Natural Language Processing (NLP)
- Language representation in NLP
- - Bag of words
- - Language models
- Feature extraction in NLP
- Sequence-based NLP models
- - LSTM
- - GRU
- - Transformer
- NLP models for specific tasks
- - Text classification
- - Text generation
- - Machine translation
- Applications of NLP
- - Chatbots
- - Sentiment analysis
- - Text summarization
UNIT 4. COMPUTER VISION
- Computer vision
- Image preprocessing and transformation
- - Filters
- - Geometric transformations
- Object detection and recognition
- - Edge detection
- - Feature descriptors
- - Object classifiers
- Image segmentation and classification
- - Semantic segmentation
- - Region-based segmentation
- - Image classification with CNN
- Application of computer vision
- - Facial recognition
- - Autonomous driving
- - Augmented reality
UNIT 5. BIG DATA PROCESSING IN ARTIFICIAL INTELLIGENCE
- Big data in artificial intelligence
- Distributed storage and processing
- - Distributed file systems
- - Hadoop
- - Spark
- Technologies and tools for big data processing
- - MapReduce
- - Pig
- - Hive
- Knowledge extraction from big data
- - Data mining
- - Graph analysis
- Machine learning in big data
- - Distributed learning
- - Mini-batch
- - Stochastic Gradient Descent (SGD)
UNIT 6. OPTIMIZATION AND FINE-TUNING OF AI MODELS
- Model evaluation and performance metrics
- Hyperparameter optimization
- - Grid search
- - Random search
- - Bayesian optimization
- Regularization and overfitting prevention techniques
- Dimensionality reduction techniques
- - Principal Component Analysis (PCA)
- - Feature Selection
- Model tuning and ensemble methods
UNIT 7. REINFORCEMENT LEARNING
- Reinforcement learning
- Agents and environments in reinforcement learning
- Reinforcement learning methods
- - Q-Learning
- - SARSA
- - Actor-Critic
- Exploration and exploitation in reinforcement learning
- Applications of reinforcement learning
- - Games
- - Robotics
UNIT 8. DEPLOYMENT AND PRODUCTION OF AI MODELS
- Data preparation for model deployment
- Design and implementation of AI services
- Monitoring and evaluation of models in production
- Updating and maintenance of AI models
- Scalability and performance in AI model deployment
Metodología
EDUCA LXP se basa en 6 pilares
Titulación

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Course on Techniques and Applications of Artificial Intelligence