Thanks to this 100% online Postgraduate Diploma, you will be nurtured with essential skills and knowledge to lead innovation in contemporary Design"

Increasingly, Virtual Assistants play a relevant role in interactive design, by facilitating the interaction between humans and digital systems in a more efficient way. In addition, these advanced resources serve to personalize the experiences of audiences by adapting to their preferences and particular needs. One example of this is that algorithms offer content and recommendations tailored to their interests. These systems also provide real-time assistance to users, guiding them through complex processes such as making an online purchase.  

In this context, TECH launches a Postgraduate Diploma that will address in depth the technologies applied to Design and AI. In this way, the syllabus will focus on the automatic detection and correction of visual errors through Machine Learning. Likewise, the syllabus will provide students with tools for usability evaluation of interface designs and optimization of editorial workflows with algorithms. On the other hand, the university program will delve into mass customization in industrial production, bearing in mind the logistical and scale challenges. The program will also analyze Machine Learning models for predictive maintenance, as well as for the evaluation of the accuracy and effectiveness of these models in industrial environments. 

Students will have video summaries of each topic, specialized readings or case study simulation scenarios that can be conveniently accessed from a digital device at any time of the day. Likewise, the Relearningsystem, based on the reiteration of content, will lead the professionals to progress naturally through the main concepts of this program and thus reduce the long hours of study.  A program that provides the flexibility that the ophthalmologist requires to make their daily work and personal activities compatible with a quality program, developed by an excellent team of specialists with a high level of competence in this field. 

The current importance of design makes this program a safe bet, with a market in continuous growth and full of possibilities" 

This Postgraduate diploma in Innovation in Design through Artificial Intelligence 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 Innovation in Design through AI

• The graphic, schematic and practical contents of the book provide theoretical 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 analyze the relationship between Artificial Intelligence and process optimization in the field of industrial design through innovative multimedia resources"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this 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.

A university program that will take you into a world of unlimited creative and personalized opportunities"

The Relearning system applied by TECH in its programs reduces the long hours of study so frequent in other teaching methods"

The present study will allow experts to delve into the dynamic fusion between the creativity of Design and the limitless potential of Machine Learning. Structured in 3 modules, the syllabus will cover from the automatic generation of visual content to the optimization of processes in Industrial Design. Professionals will delve into aspects such as the integration of the Internet of Things (IoT) and the practical application of emerging technologies. This will enable them to be highly qualified to apply AI effectively in their design processes.

You have multiple case studies that will elevate your competencies in your praxis as a designer"

Module 1. Practical Applications of Artificial Intelligence in Design

1.1. Automatic Image Generation in Graphic Design

1.1.1. Fundamental Concepts of Image Generation
1.1.2. Tools andFrameworks for Automatic Graphic Generation
1.1.3. Social and Cultural Impact of Generative Design
1.1.4. Current Trends in the Field and Future Developments and Applications

1.2. Dynamic Personalization of User Interfaces Using AI

1.2.1. UI/UX Personalization Principles
1.2.2. Recommendation Algorithms in UI Customization
1.2.3. User Experience and Continuous Feedback
1.2.4. Practical Implementation in Real Applications

1.3. Generative Design: Applications in Industry and Art

1.3.1. Fundamentals of Generative Design
1.3.2. Generative Design in Industry
1.3.3. Generative Design in Contemporary Art
1.3.4. Challenges and Future Advances in Generative Design

1.4. Automatic Creation of Editorial Layouts with Algorithms

1.4.1. Principles of Automatic Editorial Layout
1.4.2. Content Distribution Algorithms
1.4.3. Optimization of Spaces and Proportions in Editorial Design
1.4.4. Automation of the Revision and Adjustment Process

1.5. Procedural Generation of Content in Videogames

1.5.1. Introduction to Procedural Generation in Videogames
1.5.2. Algorithms for the Automatic Creation of Levels and Environments
1.5.3. Procedural Narrative and Branching in Videogames
1.5.4. Impact of Procedural Generation on the Player Experience

1.6. Pattern Recognition in Logos with Machine Learning

1.6.1. Fundamentals of Pattern Recognition in Graphic Design
1.6.2. Implementation of Machine Learning Models for Logo Identification
1.6.3. Practical Applications in Graphic Design
1.6.4. Legal and Ethical Considerations in Logo Recognition

1.7. Optimization of Colors and Compositions with AI

1.7.1. Color Psychology and Visual Composition
1.7.2. Color Optimization Algorithms in Graphic Design
1.7.3. Automatic Composition of Visual Elements
1.7.4. Evaluation of the Impact of Automatic Optimization on User Perception

1.8. Predictive Analysis of Visual Trends in Design

1.8.1. Data Collection and Current Trends
1.8.2. Machine Learning Models for Trend Prediction
1.8.3. Implementation of Proactive Design Strategies
1.8.4. Principles in the Use of Data and Predictions in Design

1.9. AI-assisted Collaboration in Design Teams

1.9.1. Human-IA Collaboration in Design Projects
1.9.2. Platforms and Tools for AI-assisted Collaboration
1.9.3. Best Practices in AI-assisted Technology Integration
1.9.4. Future Perspectives on Human-AI Collaboration in Design

1.10. Strategies for the Successful Incorporation of AI in Design

1.10.1. Identification of AI-solvable Design Needs
1.10.2. Evaluation of Available Platforms and Tools
1.10.3. Effective Integration in Design Projects
1.10.4. Continuous Optimization and Adaptability

Module 2. Innovation in Design and AI Processes

2.1. Optimization of Manufacturing Processes with AI Simulations

2.1.1. Introduction to Manufacturing Process Optimization
2.1.2. AI Simulations for Production Optimization
2.1.3. Technical and Operational Challenges in the Implementation of AI Simulations
2.1.4. Future Perspectives: Advances in Process Optimization with AI

2.2. Virtual Prototyping: Challenges and Benefits

2.2.1. Importance of Virtual Prototyping in Design
2.2.2. Tools and Technologies for Virtual Prototyping
2.2.3. Challenges in Virtual Prototyping and Strategies for Overcoming them
2.2.4. Impact on Design Innovation and Agility

2.3. Generative Design: Applications in Industry and Artistic Creation

2.3.1. Architecture and Urban Planning
2.3.2. Fashion and Textile Design
2.3.3. Design of Materials and Textures
2.3.4. Automation in Graphic Design

2.4. Materials and Performance Analysis using Artificial Intelligence

2.4.1. Importance of Materials and Performance Analysis in Design
2.4.2. Artificial Intelligence Algorithms for Materials Analysis
2.4.3. Impact on Design Efficiency and Sustainability
2.4.4. Implementation Challenges and Future Applications

2.5. Mass Customization in Industrial Production

2.5.1. Transforming Production through Mass Customization
2.5.2. Enabling Technologies for Mass Customization
2.5.3. Logistical and Scale Challenges of Mass Customization
2.5.4. Economic Impact and Innovation Opportunities

2.6. Artificial Intelligence-assisted Design Tools

2.6.1. Design Aided Design by Gan Generation ( Generative Adversarial Networks)
2.6.2. Collective Idea Generation
2.6.3. Context-aware Generation
2.6.4. Exploration of Non-linear Creative Dimensions

2.7. Human-robot Collaborative Design in Innovative Projects

2.7.1. Integration of Robots in Innovative Design Projects
2.7.2. Tools and Platforms for Human-Robot Collaboration
2.7.3. Challenges in the Integration of Robots in Creative Projects
2.7.4. Future Perspectives in Collaborative Design with Emerging Technologies

2.8. Predictive Maintenance of Products: AI Approach

2.8.1. Importance of Predictive Maintenance in Product Life Extension
2.8.2. Machine Learning Models for Predictive Maintenance
2.8.3. Practical Implementation in Various Industries
2.8.4. Evaluation of the Accuracy and Effectiveness of these Models in Industrial Environments

2.9. Automatic Generation of Typefaces and Visual Styles

2.9.1. Basics of Automatic Generation in Typeface Design
2.9.2. Practical Applications in Graphic Design and Visual Communication
2.9.3. AI-assisted Collaborative Design in the Creation of Typefaces
2.9.4. Exploration of Automatic Styles and Trends

2.10. IoT Integration to Monitor Products in Real Time

2.10.1. Transformation with IoT Integration in Product Design
2.10.2. Sensors and IoT Devices for Real-Time Monitoring
2.10.3. Data Analysis and IoT-based Decision Making
2.10.4. Implementation Challenges and Future Applications of IoT in Design

Module 3. Technologies Applied to Design and AI

3.1. Integration of Virtual Assistants in Design Interfaces

3.1.1. Role of Virtual Assistants in Interactive Design
3.1.2. Development of Virtual Assistants Specialized in Design
3.1.3. Natural Interaction with Virtual Assistants in Design Projects
3.1.4. Implementation Challenges and Continuous Improvement

3.2. Automatic visual error detection and correction with AI

3.2.1. Importance of Automatic Visual Error Detection and Correction
3.2.2. Algorithms and Models for Visual Error Detection
3.2.3. Automatic Correction Tools in Visual Design
3.2.4. Challenges in Automatic Error Detection and Correction and Strategies to Overcome them

3.3. AI Tools for Usability Evaluation of Interface Designs

3.3.1. Analysis of Interaction Data with Machine Learning Models
3.3.2. Automated Report Generation and Recommendations
3.3.3. Virtual User Simulations for Usability Testing
3.3.4. Conversational Interface for User Feedback

3.4. Optimization of Editorial Workflows with Algorithms

3.4.1. Importance of Optimizing Editorial Workflows
3.4.2. Algorithms for Editorial Automation and Optimization
3.4.3. Tools and Technologies for Editorial Optimization
3.4.4. Challenges in Implementation and Continuous Improvement in Editorial Workflows

3.5. Realistic Simulations in Video Game Design

3.5.1. Importance of Realistic Simulations in the Video Game Industry
3.5.2. Modeling and Simulation of Realistic Elements in Video Games
3.5.3. Technologies and Tools for Realistic Simulations in Videogames
3.5.4. Technical and Creative Challenges in Realistic Video Game Simulations

3.6. Automatic Generation of Multimedia Content in Editorial Design

3.6.1. Transformation with Automatic Multimedia Content Generation
3.6.2. Algorithms and Models for Automatic Multimedia Content Generation
3.6.3. Practical Applications in Publishing Projects
3.6.4. Challenges and Future Trends in the Automatic Generation of Multimedia Content

3.7. Adaptive and Predictive Design based on User Data

3.7.1. Importance of Adaptive and Predictive Design in User Experience
3.7.2. Collection and Analysis of User Data for Adaptive Design
3.7.3. Algorithms for Adaptive and Predictive Design
3.7.4. Integration of Adaptive Design in Platforms and Applications

3.8. Integration of Algorithms in the Improvement of Usability

3.8.1. Segmentation and Behavioral Patterns
3.8.2. Detection of Usability Problems
3.8.3. Adaptability to Changes in User Preferences
3.8.4. Automated a/b Testing and Analysis of Results

3.9. Continuous Analysis of User Experience for Iterative Improvements

3.9.1. Importance of Continuous Feedback in Product and Service Evolution
3.9.2. Tools and Metrics for Continuous Analysis
3.9.3. Case Studies Demonstrating Substantial Improvements Achieved through this Approach
3.9.4. Handling of Sensitive Data

3.10. AI-assisted Collaboration in Editorial Teams

3.10.1. Transformation of AI-assisted Collaboration in Editorial Teams
3.10.2. Tools and Platforms for AI-assisted Collaboration
3.10.3. Development of Virtual Assistants Specialized in Editing
3.10.4. Challenges in the Implementation and Future Applications of AI-assisted Collaboration

You will have access to a library of multimedia resources 7 days a week, 24 hours a day"