Transformación digital e Infraestructuras Inteligentes
Master´s Degree in Artificial Intelligence and Big Data
Inglés
Créditos 60 ECTS
PRESENTACIÓN
Objetivos
The overall objective of this program is to guide the student towards developing a new technological profile and acquiring the management and software project development skills necessary in this changing world. The global objective consist of the following specific objectives:
- Understand what Big Data is, why talk about Big Data, what it is for and the technological elements that support the concept.
- Understand intelligent systems capable of responding to current demands.
- Know and design the architecture behind Big Data and Artificial Intelligence
- Develop ideation and management methodologies for AI projects
- Identify the factors that convert an AI solution into a viable project.
Metodología
En Educa PHAROS trabajamos con una metodología eminentemente práctica, orientada a la aplicación directa de los conocimientos en el entorno profesional del alumno, combinando contenidos teóricos con casos reales, herramientas digitales y el acompañamiento de un equipo docente especializado.
Programa
State of the art of artificial intelligence
Philosophy of artificial intelligence
Future of artificial intelligence
Project development process with artificial intelligence
Data, your greatest asset
Machine learning
Deep learning
Transformers
Generation of synthetic data
Hyperparameters in artificial intelligence models
Linear regression
Non-linear regression and support vector machines (SVM)
Decision trees, random forests
Fuse logic and gradient down
Recommendation systems
Preparation of the working environment: Anaconda, Visual Studio Code and Python
Input dataset and data preprocessing
TensorHub, TensorFlow and Keras
Image processing
Generation of artificial intelligence models
Introduction
Simple, multiple and logistic linear regression (I)
Simple, multiple and logistic linear regression (II)
Support vector machines (SVM)
Decision trees
KNN (k-nearest neighbors)
Naive Bayes
Evaluation of supervised models
Example exercise
Proposed exercise
Introduction to clustering. purconsider and metrics
K-means clustering
Hierarchical clustering, other techniques and examples
Principal component analysis (PCA)
PCA example exercise
Artificial Neural Networks (ANN) (I)
Artificial Neural Networks (ANN) (II)
Artificial Neural Networks (ANN) (III)
Example exercise
Proposed exercise
Introduction
Review. Artificial neural network (ANN)
Review. ANN exercises
Convolutional Neural Networks (CNN)
CNN Exercises
Natural language processing (I)
Recurrent neural networks (RNN) (I)
Recurrent neural networks (RNN) (II)
Natural language processing (II)
RNN Exercise
Boltzmann Machines (BM)
Restricted Boltzmann Machines (RBM)
Recommender systems
Recommender systems. metrics
RBM exercise
Self-organizing maps (SOM)
SOM exercises
Autoencoders (AE)
AE exercises
Proposed exercise
The fourth industrial revolution
Digital transformation in companies
Fundaments and key points
Benefits
Enabling technologies
Big data
Cloud computing
Blockchain
Artificial intelligence
Augmented and virtual reality
BIM
Collaborative robots
Additive manufacturing
Hyperconnectivity
IoT
Manufacturing execution system (MES)
Process integration and efficiency
Use cases
New methodologies. agile, lean startup or design thinking
Business change management
Introduction
Key elements in AI project management
AI project characteristics
Introduction to the main agile and ideation methodologies
Methodology integration
Introduction
Phase I. Empathize
Phase II. Define
Phase III. Devise
Phase IV. Prototype
Lean start-up. Basic concepts
Lean start-up. Tools
Scrum. Introduction
Scrum. Roles
Scrum. Ceremonies and artifacts
Introduction
Project ideation
Project implementation
Advise on implementing methodologies
Summary and conclusions
Financial sector
Retail sector
Industrial sector
Agricultural sector
Health sector
Logistics and operations
Marketing
Sales and customer service
Finance and control
People analytics
Current scenario of a booming sector
Financing
Featured start-ups
Future of the AI ecosystem
Starting an AI company
Ethics. General remarks
Bias examples
Global initiatives
Public Institutions and regulations
AI in the SDGs
Data information knoledge wisdom
Data management (i)
Data management (ii)
Corporate performance management
Databases
Business intelligence
Datawarehousing
Big data
Hadoop
Spark
Hadoop ecosystem (I)
Hadoop ecosystem (II)
Hadoop ecosystem (III)
Spark ecosystem
Installation and configuration of big data architectures
Educa PHAROS es una solución formativa para empresas que centraliza el aprendizaje, facilita la gestión de usuarios y permite medir el progreso mediante paneles y reportes.
Está dirigido a organizaciones que desean desarrollar habilidades internas, estandarizar la formación por roles y disponer de trazabilidad y métricas de aprendizaje.
Incluye itinerarios por perfiles, gestión de usuarios y permisos, reportes de progreso, certificación, y acceso a masterclass o sesiones en vivo (según plan).
Se apoya en buenas prácticas de seguridad, control de accesos, trazabilidad del aprendizaje y soporte de implementación. Personaliza este texto según tus compromisos (SLA, compliance, etc.).
Educa PHAROS es un modelo formativo de nueva generación que posiciona al capital humano de la empresa a la vanguardia. A través de una plataforma que se adapta a la imagen corporativa de cada empresa y con un total de más de 900 cursos se consigue una formación específica para cada organización. La tarifa plana ilimitada, proporciona a cada empresa el número de cursos que se ajuste a sus necesidades y también la posibilidad de determinar qué empleados podrán tener acceso.
Transformación digital e Infraestructuras Inteligentes 
Inglés 
Créditos 60 ECTS
Industria, Energía y Medio Ambiente 
Inglés
60 ECTS