Transformación digital e Infraestructuras Inteligentes
Master´s Degree in Artificial Intelligence. Model Management and Implementation
Inglés
Créditos 60 ECTS
PRESENTACIÓN
Objetivos
The overall objective is to equip students with the necessary knowledge and tools to understand, manage and lead IA initiatives and projects in an organization.
The overall objective can be attained through the following specific objective:
1. Understand AI basic concepts, its limits, and possibilities.
2. Learn AI project programming languages, tools and platforms
3. Become familiar with the most used Machine Learning and Deep Learning algorithms
4. Analyze and learn other technologies to be able to develop innovative and differential business models.
5. Learn the main AI project management methodologies
6. Understand the implications and applications of AI in the different functional areas of a company
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
Self - Service solutions
Data processing techniques
Data quality management
Types of data problem
Data cleaning with Excel
DATASET
Functions. Part I
Functions. Part II
Functions. Part III
Instructions for installing talend data preparation free desktop
Data Cleansing with Talend Data Preparation
Basic cleansing functions
Data normalization
Data enrichment
Registration instructions
Data cleansing with trifacta
Basic cleansing functions
Data normalization
Data enrichment
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
Introduction to Power BI
Different types of Power BI
First simple report
Power Query: Data Sources
Data Transformation
Data Modeling
Starting with DAX (I)
Starting with DAX (II)
Getting proficient in DAX (I)
Getting proficient in DAX (II)
Table and Matrix
Trends
How to properly filter your data
Bookmarks
Drill through
Understanding Power BI Service in depth
Sharing content in Power BI Service
Comparing Power BI Service and Power BI Report Server
Integrating Python and R in Power BI Desktop
Introducing Bravo for Power BI Desktop
Linear regression
Logistic regression
Basic Neural Network
Clustering
Principal Component Analysis (PCA)
Deep learning
Optimization
Convolutional Neural Network
Recurrent Neural Network
Natural Language Processing (NLP)
Creating tables and Reports
Transformation and filtering data
Data visualization
Relation between data tables
Dashboard
Object detection in images
Object classification in images
Facial recognition
Word detection
Business Intelligence application
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
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