The application of Artificial Intelligence in Designwill allow you to access a more innovative, user-centered creative process. What are you waiting for to enroll?" 

The synergy between Artificial Intelligence and Design has generated a true revolution in the conception and development of projects in this field. A key point to take into account is the substantial improvement of the creative process: AI algorithms explore vast data sets to discover patterns and trends, providing invaluable insights that drive decision making in the field of Design.

In this context, TECH presents this professional master’s degree in Artificial Intelligence in Design, which seamlessly merges new technologies with the creation of creative products, providing designers with a unique and comprehensive perspective. In addition to imparting technical knowledge, this program will address ethics and sustainability, ensuring that graduates are prepared to face contemporary challenges in a constantly evolving field.

Similarly, the breadth of topics to be covered reflects the diversity of applications of AI in different disciplines, from automated content generation to strategies to reduce waste in the Design process. In fact, the emphasis on ethics and environmental impact is designed to train conscious and competent professionals.

Finally, it will cover data analysis for decision making in Design, the implementation of AI systems to personalize products and experiences, as well as the exploration of advanced visualization techniques and creative content generation.
In this way, TECHhas designed a rigorous academic program, supported by the innovative Relearningmethod. This educational approach consists of reiterating key concepts to ensure a deep understanding of the content. Accessibility is also key, since it is enough to have an electronic device connected to the Internet to access the material at any time and in any place, freeing the students from the limitations of physically attending or adjusting to predefined schedules. 

You'll tackle the integration of AI into Design, boosting efficiency and personalization and opening the door to new creative possibilities”

This professional master’s degree in Artificial Intelligence in Design contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Artificial Intelligence in Design
• The graphic, schematic and practical contents of the book provide technical and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning 
• Its special emphasis on innovative methodologies  
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments 
• Content that is accessible from any fixed or portable device with an Internet connection

You will explore the complex intersection between ethics, the environment and new technologies in depth through this unique professional master’s degree, taught entirely online”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

From visual creation automation, to predictive trend analysis and AI-powered collaboration, you'll be immersed in a dynamic field"

Take advantage of TECH's vast library of multimedia resources and explore the fusion of virtual assistants and user emotion analysis"

The main purpose of this professional master’s degree is to provide designers with a thorough and complete understanding of the intersection between Artificial Intelligence and the field of Design. This will involve not only strengthening their technical and creative skills, but also conceiving and applying AI algorithms in innovative processes. In addition, a critical and ethical vision in the use of AI in creative projects will be promoted, preparing professionals to face ethical dilemmas and emerging social challenges. Topics ranging from the personalization of user experiences to the generation of visual content will also be addressed. 

You will lead in a context where collaboration between human inventiveness and cutting-edge technology is fundamental to the development of today's Design”

General Objectives

• Understand the theoretical foundations of Artificial Intelligence
• Study the different types of data and understand the data lifecycle 
• Evaluate the crucial role of data in the development and implementation of AI solutions 
• Delve into algorithms and complexity to solve specific problems 
• Explore the theoretical basis of neural networks for Deep Learning development 
• Analyze bio-inspired computing and its relevance in the development of intelligent systems
• Analyze current strategies of Artificial Intelligence in various fields, identifying opportunities and challenges
• Develop skills to implement artificial intelligence tools in design projects, including automatic content generation, design optimization and pattern recognition
• Apply collaborative tools, taking advantage of Artificial Intelligence to improve communication and efficiency in design teams
• Incorporate emotional aspects into designs through techniques that effectively connect with the audience
• Understand the symbiosis between interactive design and Artificial Intelligence to optimize the user experience
• Develop skills in adaptive design, considering user behavior and applying advanced AI tools 
• Critically analyze the challenges and opportunities when implementing personalized designs in industry using Artificial Intelligence 
• Understand the transformative role of Artificial Intelligence in design and manufacturing process innovation

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence  

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems
• Explore the concept of the semantic web and its influence on the organization and understanding of information in digital environments

Module 2. Data Types and Data Life Cycle  

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data 
• Analyze the life cycle of data, from generation to disposal, identifying key stages 
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure 
• Study data collection processes, including methodology, tools and collection channels 
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design 
• Analyze the regulatory aspects related to data management, complying with privacy and security regulations, as well as best practices

Module 3. Data in Artificial Intelligence  

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Analyze supervised and unsupervised models, including methods and classification 
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence 

Module 4. Data Mining. Selection, Pre-Processing and Transformation 

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends 
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining 
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context 
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set 
• Address data preprocessing in Big Data environments 

Module 5. Algorithm and Complexity in Artificial Intelligence  

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving 
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space 
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts 
• Explore tree-based algorithms, understanding their structure and applications 
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation 
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships 
• Study Greedyalgorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems  

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems 
• Study semantic reasoners, knowledge-based systems and expert systems, understanding their functionality and applications in intelligent decision making

Module 7. Machine Learning and Data Mining 

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems 
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers 
• Analyze regression and continuous response models for predicting numerical values from data 
• Study clustering techniques to identify patterns and structures in unlabeled data sets 
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text 

Module 8. Neural Networks, the Basis of Deep Learning  

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning 
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately 
• Understanding the effective linking of layers and operations to design complex and efficient neural network architectures 
• Use trainers and optimizers to tune and improve the performance of neural networks 
• Explore the connection between biological and artificial neurons for a deeper understanding of model design 
• Tuning hyperparameters for Fine Tuning of neural networks, optimizing their performance on specific tasks 

Module 9. Deep Neural Networks Training  

• Solve gradient-related problems in deep neural network training 
• Explore and apply different optimizers to improve the efficiency and convergence of models 
• Program the learning rate to dynamically adjust the convergence speed of the model 
• Understand and address overfitting through specific strategies during training 
• Apply practical guidelines to ensure efficient and effective training of deep neural networks 
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks 
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization 
• Develop practical applications using Transfer Learning to solve real-world problems 
• Understand and apply regularization techniques to improve generalization and avoid overfitting in deep neural networks 

Module 10. Model Customization and Training with TensorFlow  

• Master the fundamentals of TensorFlow and its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow 
• Explore the tfdata API to efficiently manage and manipulate datasets 
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow 
• Use Keras preprocessing layers to facilitate the construction of custom models 
• Explore the TensorFlow Datasetsproject to access predefined datasets and improve development efficiency 
• Develop a Deep Learning  application with TensorFlow, integrating the knowledge acquired in the module 
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations 

Module 11. Deep Computer Vision with Convolutional Neural Networks  

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision 
• Explore and apply convolutional layers to extract key features from images 
• Implement clustering layers and their use in  Deep Computer Vision models with Keras 
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts 
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance 
• Use pre-trained Keras models to leverage transfer learning for specific tasks 
• Apply classification and localization techniques in Deep Computer Vision environments 
• Explore object detection and object tracking strategies using Convolutional Neural Networks 
• Implement semantic segmentation techniques to understand and classify objects in images in a detailed manner 

Module 12. Natural Language Processing (NLP) with Natural Recurrent Networks (NNN) and Attention

• Developing skills in text generation using Recurrent Neural Networks (RNN) 
• Apply RNNs in opinion classification for sentiment analysis in texts 
• Understand and apply attentional mechanisms in natural language processing models 
• Analyze and use Transformers models in specific NLP tasks 
• Explore the application of Transformers models in the context of image processing and computer vision 
• Become familiar with the Hugging Face  Transformers library for efficient implementation of advanced models 
  Compare different Transformers libraries to evaluate their suitability for specific tasks 
  Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems 

Module 13. Autoencoders, GANs, and Diffusion Models  

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation 
• Implement and understand the operation of stacked autoencoders 
• Explore and apply convolutional autoencoders for efficient visual data representations 
• Analyze and apply the effectiveness of sparse automatic encoders in data representation 
• Generate fashion images from the MNIST dataset using Autoencoders 
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models 
• Implement and compare the performance of Diffusion Models and GANs in data generation 

Module 14. Bio-Inspired Computing   

• Introduce the fundamental concepts of bio-inspired computing
• Explore social adaptation algorithms as a key approach in bio-inspired computing 
• Analyze space exploration-exploitation strategies in genetic algorithms 
• Examine models of evolutionary computation in the context of optimization  
• Continue detailed analysis of evolutionary computation models  
• Apply evolutionary programming to specific learning problems 
• Address the complexity of multi-objective problems in the framework of bio-inspired computing 
• Explore the application of neural networks in the field of bio-inspired computing  
• Delve into the implementation and usefulness of neural networks in bio-inspired computing 

Module 15. Artificial Intelligence: Strategies and Applications 

• Develop strategies for the implementation of artificial intelligence in financial services
• Analyze the implications of artificial intelligence in the delivery of healthcare services 
• Identify and assess the risks associated with the use of AI in the healthcare field 
• Assess the potential risks associated with the use of AI in industry 
• Apply artificial intelligence techniques in industry to improve productivity 
• Design artificial intelligence solutions to optimize processes in public administration 
• Evaluate the implementation of AI technologies in the education sector 
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity 
• Optimize human resources processes through the strategic use of artificial intelligence 

Module 16. Practical Applications of Artificial Intelligence in Design 

• Apply collaborative tools, leveraging AI to improve communication and efficiency in design teams
• Incorporate emotional aspects into designs through techniques that effectively connect with the audience, exploring how AI can influence the emotional perception of Design
• Master tools and frameworks specific to the application of AI in Design, such as GANs (Generative Adversarial Networks) and other relevant libraries 
• Employ AI to generate images, illustrations and other visual elements automatically  
• Implementing AI techniques to analyze design-related data, such as navigation behavior and user feedback 

Module 17. Design-User Interaction and AI 

• Understand the symbiosis between Interactive Design and AI to optimize the user experience
• Develop skills in Adaptive Design, considering user behavior and applying advanced AI tools 
• Critically analyze the challenges and opportunities when implementing personalized designs in industry using AI
• Use predictive AI algorithms to anticipate user interactions, enabling proactive and efficient design responses 
• Develop AI-based recommender systems that suggest relevant content, products or actions to users

Module 18. Innovation in Design and AI Processes 

• Understand the transformative role of AI in design and manufacturing process innovation
• Implement mass customization strategies in production through Artificial Intelligence, adapting products to individual needs
• Apply AI techniques to minimize waste in the Design process, contributing to more sustainable practices 
• Develop practical skills to apply AI techniques to improve industrial and design processes
• Encourage creativity and exploration during design processes, using AI as a tool to generate innovative solutions

Module 19. Technologies Applied to Design and AI 

• Enhance comprehensive understanding and practical skills to leverage advanced technologies and Artificial Intelligence in various facets of Design
• Understand the strategic integration of emerging technologies and AI in the Design domain
• Apply microchip architecture optimization techniques using AI to improve both performance and efficiency 
• Properly use algorithms for the automatic generation of multimedia content, enriching visual communication in editorial projects 
• Implement the knowledge and skills acquired during this program to real projects involving technologies and AI in Design

Module 20. Ethics and Environment in Design and AI 

• Understand the ethical principles related to Design and Artificial Intelligence, cultivating an ethical awareness in decision making 
• Focus on the ethical integration of technologies, such as emotion recognition, ensuring immersive experiences that respect the user's privacy and dignity 
• Promote social and environmental responsibility in Game Design and in the industry in general, considering ethical aspects in representation and gameplay 
• Generate sustainable practices in design processes, ranging from waste reduction to the integration of responsible technologies, contributing to the preservation of the environment
• Analyze how AI technologies can affect society, considering strategies to mitigate their possible negative impacts

You will harness the potential of AI in optimizing creative processes and creating innovative and responsible Design solutions”