The application of Artificial Intelligence in the field of Design will allow you to access a more innovative, user-centered creative process”

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 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, TECH has designed a rigorous academic program, supported by the innovative Relearning method. 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 fixed schedules. In addition, students will have access to an exclusive series of 10 complementary Masterclasses, designed by an internationally renowned expert in Artificial Intelligence and Machine Learning. 

You will have the opportunity to take part in exclusive Masterclasses delivered by a renowned international expert in Artificial Intelligence and Machine Learning” 

This 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 with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice 
• Practical exercises where the self-assessment process can be carried out to improve learning 
• Special emphasis on innovative methodologies in Artificial Intelligence in Design 
• 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 Master's Degree, taught entirely online” 

Its teaching staff includes professionals from the field of Artificial Intelligence in Design, who bring their work experience to this program, as well as renowned specialists from leading companies 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 an immersive learning experience designed to prepare for real-life situations. 

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional 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”

What makes this Master's Degree in Artificial Intelligence in Design exceptional is its revolutionary and comprehensive approach to the intersection between Design and Artificial Intelligence. The incorporation of specific modules such as “Computational Design and AI” and “Design-User Interaction and AI” will enable designers to address contemporary challenges, from the automatic creation of multimedia content to contextual adaptation in user interactions. In addition, the innovative fusion of technical skills, such as microchip structure optimization, with ethical and ecological considerations, such as waste minimization, makes this program a comprehensive proposition. 

Immerse yourself in a training that integrates creativity with a deep focus on ethics and sustainability, applying Artificial Intelligence in the field of Design” 

Module 1. Fundamentals of Artificial Intelligence 

1.1. History of Artificial Intelligence 

1.1.1. When Do We Start Talking About Artificial Intelligence? 
1.1.2. References in Film 
1.1.3. Importance of Artificial Intelligence 
1.1.4. Technologies that Enable and Support Artificial Intelligence 

1.2. Artificial Intelligence in Games 

1.2.1. Game Theory 
1.2.2. Minimax and Alpha-Beta Pruning 
1.2.3. Simulation: Monte Carlo 

1.3. Neural Networks 

1.3.1. Biological Fundamentals 
1.3.2. Computational Model 
1.3.3. Supervised and Unsupervised Neural Networks 
1.3.4. Simple Perceptron 
1.3.5. Multilayer Perceptron 

1.4. Genetic Algorithms 

1.4.1. History 
1.4.2. Biological Basis 
1.4.3. Problem Coding 
1.4.4. Generation of the Initial Population 
1.4.5. Main Algorithm and Genetic Operators 
1.4.6. Evaluation of Individuals: Fitness 

1.5. Thesauri, Vocabularies, Taxonomies 

1.5.1. Vocabulary 
1.5.2. Taxonomy 
1.5.3. Thesauri 
1.5.4. Ontologies 
1.5.5. Knowledge Representation: Semantic Web 

1.6. Semantic Web 

1.6.1. Specifications: RDF, RDFS and OWL 
1.6.2. Inference/ Reasoning 
1.6.3. Linked Data 

1.7. Expert Systems and DSS 

1.7.1. Expert Systems 
1.7.2. Decision Support Systems 

1.8. Chatbots and Virtual Assistants 

1.8.1. Types of Assistants: Voice and Text Assistants 
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow 
1.8.3. Integrations: Web, Slack, WhatsApp, Facebook 
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant 

1.9. AI Implementation Strategy 
1.10. Future of Artificial Intelligence 

1.10.1. Understand How to Detect Emotions Using Algorithms 
1.10.2. Creating a Personality: Language, Expressions and Content 
1.10.3. Trends of Artificial Intelligence 
1.10.4. Reflections 

Module 2. Data Types and Life Cycle 

2.1. Statistics 

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences 
2.1.2. Population, Sample, Individual 
2.1.3. Variables: Definition, Measurement Scales 

2.2. Types of Data Statistics 

2.2.1. According to Type 

2.2.1.1. Quantitative: Continuous Data and Discrete Data 
2.2.1.2. Qualitative. Binomial Data, Nominal Data and Ordinal Data 

2.2.2. According to Its Shape 

2.2.2.1. Numeric 
2.2.2.2. Text 
2.2.2.3. Logical 

2.2.3. According to Its Source 

2.2.3.1. Primary 
2.2.3.2. Secondary 

2.3. Life Cycle of Data 

2.3.1. Stages of the Cycle 
2.3.2. Milestones of the Cycle 
2.3.3. FAIR Principles 

2.4. Initial Stages of the Cycle 

2.4.1. Definition of Goals 
2.4.2. Determination of Resource Requirements 
2.4.3. Gantt Chart 
2.4.4. Data Structure 

2.5. Data Collection 

2.5.1. Methodology of Data Collection 
2.5.2. Data Collection Tools 
2.5.3. Data Collection Channels 

2.6. Data Cleaning 

2.6.1. Phases of Data Cleansing 
2.6.2. Data Quality 
2.6.3. Data Manipulation (with R) 

2.7. Data Analysis, Interpretation and Evaluation of Results 

2.7.1. Statistical Measures 
2.7.2. Relationship Indexes 
2.7.3. Data Mining 

2.8. Datawarehouse 

2.8.1. Elements that Comprise It 
2.8.2. Design 
2.8.3. Aspects to Consider 

2.9. Data Availability 

2.9.1. Access 
2.9.2. Uses 
2.9.3. Security 

2.10. Regulatory Framework 

2.10.1. Data Protection Law 
2.10.2. Good Practices 
2.10.3. Other Regulatory Aspects 

Module 3. Data in Artificial Intelligence 

3.1. Data Science 

3.1.1. Data Science 
3.1.2. Advanced Tools for Data Scientists 

3.2. Data, Information and Knowledge 

3.2.1. Data, Information and Knowledge 
3.2.2. Types of Data 
3.2.3. Data Sources 

3.3. From Data to Information 

3.3.1. Data Analysis 
3.3.2. Types of Analysis 
3.3.3. Extraction of Information from a Dataset 

3.4. Extraction of Information Through Visualization 

3.4.1. Visualization as an Analysis Tool 
3.4.2. Visualization Methods 
3.4.3. Visualization of a Data Set 

3.5. Data Quality 

3.5.1. Quality Data 
3.5.2. Data Cleaning 
3.5.3. Basic Data Pre-Processing 

3.6. Dataset 

3.6.1. Dataset Enrichment 
3.6.2. The Curse of Dimensionality 
3.6.3. Modification of Our Data Set 

3.7. Unbalance 

3.7.1. Classes of Unbalance 
3.7.2. Unbalance Mitigation Techniques 
3.7.3. Balancing a Dataset 

3.8. Unsupervised Models 

3.8.1. Unsupervised Model 
3.8.2. Methods 
3.8.3. Classification with Unsupervised Models 

3.9. Supervised Models 

3.9.1. Supervised Model 
3.9.2. Methods 
3.9.3. Classification with Supervised Models 

3.10. Tools and Good Practices 

3.10.1. Good Practices for Data Scientists 
3.10.2. The Best Model 
3.10.3. Useful Tools 

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

4.1. Statistical Inference 

4.1.1. Descriptive Statistics vs. Statistical Inference 
4.1.2. Parametric Procedures 
4.1.3. Non-Parametric Procedures 

4.2. Exploratory Analysis 

4.2.1. Descriptive Analysis 
4.2.2. Visualization 
4.2.3. Data Preparation 

4.3. Data Preparation 

4.3.1. Integration and Data Cleaning 
4.3.2. Normalization of Data 
4.3.3. Transforming Attributes 

4.4. Missing Values 

4.4.1. Treatment of Missing Values 
4.4.2. Maximum Likelihood Imputation Methods 
4.4.3. Missing Value Imputation Using Machine Learning 

4.5. Noise in the Data 

4.5.1. Noise Classes and Attributes 
4.5.2. Noise Filtering 
4.5.3. The Effect of Noise 

4.6. The Curse of Dimensionality 

4.6.1. Oversampling 
4.6.2. Undersampling 
4.6.3. Multidimensional Data Reduction 

4.7. From Continuous to Discrete Attributes 

4.7.1. Continuous Data vs. Discreet Data 
4.7.2. Discretization Process 

4.8. The Data 

4.8.1. Data Selection 
4.8.2. Prospects and Selection Criteria 
4.8.3. Selection Methods 

4.9. Instance Selection 

4.9.1. Methods for Instance Selection 
4.9.2. Prototype Selection 
4.9.3. Advanced Methods for Instance Selection 

4.10. Data Pre-Processing in Big Data Environments 

Module 5. Algorithm and Complexity in Artificial Intelligence 

5.1. Introduction to Algorithm Design Strategies 

5.1.1. Recursion 
5.1.2. Divide and Conquer 
5.1.3. Other Strategies 

5.2. Efficiency and Analysis of Algorithms 

5.2.1. Efficiency Measures 
5.2.2. Measuring the Size of the Input 
5.2.3. Measuring Execution Time 
5.2.4. Worst, Best and Average Case 
5.2.5. Asymptotic Notation 
5.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms 
5.2.7. Mathematical Analysis of Recursive Algorithms 
5.2.8. Empirical Analysis of Algorithms 

5.3. Sorting Algorithms 

5.3.1. Concept of Sorting 
5.3.2. Bubble Sorting 
5.3.3. Sorting by Selection 
5.3.4. Sorting by Insertion 
5.3.5. Sorting by Merge (Merge_Sort) 
5.3.6. Sorting Quickly (Quick_Sort) 

5.4. Algorithms with Trees 

5.4.1. Tree Concept 
5.4.2. Binary Trees 
5.4.3. Tree Paths 
5.4.4. Representing Expressions 
5.4.5. Ordered Binary Trees 
5.4.6. Balanced Binary Trees 

5.5. Algorithms Using Heaps 

5.5.1. Heaps 
5.5.2. The Heapsort Algorithm 
5.5.3. Priority Queues 

5.6. Graph Algorithms 

5.6.1. Representation 
5.6.2. Traversal in Width 
5.6.3. Depth Travel 
5.6.4. Topological Sorting 

5.7. Greedy Algorithms 

5.7.1. Greedy Strategy 
5.7.2. Elements of the Greedy Strategy 
5.7.3. Currency Exchange 
5.7.4. Traveler’s Problem 
5.7.5. Backpack Problem 

5.8. Minimal Path Finding 

5.8.1. The Minimum Path Problem 
5.8.2. Negative Arcs and Cycles 
5.8.3. Dijkstra's Algorithm 

5.9. Greedy Algorithms on Graphs 

5.9.1. The Minimum Covering Tree 
5.9.2. Prim's Algorithm 
5.9.3. Kruskal’s Algorithm 
5.9.4. Complexity Analysis 

5.10. Backtracking 

5.10.1. Backtracking 
5.10.2. Alternative Techniques 

Module 6. Intelligent Systems 

6.1. Agent Theory 

6.1.1. Concept History 
6.1.2. Agent Definition 
6.1.3. Agents in Artificial Intelligence 
6.1.4. Agents in Software Engineering 

6.2. Agent Architectures 

6.2.1. The Reasoning Process of an Agent 
6.2.2. Reactive Agents 
6.2.3. Deductive Agents 
6.2.4. Hybrid Agents 
6.2.5. Comparison 

6.3. Information and Knowledge 

6.3.1. Difference between Data, Information and Knowledge 
6.3.2. Data Quality Assessment 
6.3.3. Data Collection Methods 
6.3.4. Information Acquisition Methods 
6.3.5. Knowledge Acquisition Methods 

6.4. Knowledge Representation 

6.4.1. The Importance of Knowledge Representation 
6.4.2. Definition of Knowledge Representation According to Roles 
6.4.3. Knowledge Representation Features 

6.5. Ontologies 

6.5.1. Introduction to Metadata 
6.5.2. Philosophical Concept of Ontology 
6.5.3. Computing Concept of Ontology 
6.5.4. Domain Ontologies and Higher-Level Ontologies 
6.5.5. How to Build an Ontology 

6.6. Ontology Languages and Ontology Creation Software 

6.6.1. Triple RDF, Turtle and N 
6.6.2. RDF Schema 
6.6.3. OWL 
6.6.4. SPARQL 
6.6.5. Introduction to Ontology Creation Tools 
6.6.6. Installing and Using Protégé 

6.7. Semantic Web 

6.7.1. Current and Future Status of the Semantic Web 
6.7.2. Semantic Web Applications 

6.8. Other Knowledge Representation Models 

6.8.1. Vocabulary 
6.8.2. Global Vision 
6.8.3. Taxonomy 
6.8.4. Thesauri 
6.8.5. Folksonomy 
6.8.6. Comparison 
6.8.7. Mind Maps 

6.9. Knowledge Representation Assessment and Integration 

6.9.1. Zero-Order Logic 
6.9.2. First-Order Logic 
6.9.3. Descriptive Logic 
6.9.4. Relationship between Different Types of Logic 
6.9.5. Prolog: Programming Based on First-Order Logic 

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems 

6.10.1. Concept of Reasoner 
6.10.2. Reasoner Applications 
6.10.3. Knowledge-Based Systems 
6.10.4. MYCIN: History of Expert Systems 
6.10.5. Expert Systems Elements and Architecture 
6.10.6. Creating Expert Systems 

Module 7. Machine Learning and Data Mining 

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning 

7.1.1. Key Concepts of Knowledge Discovery Processes 
7.1.2. Historical Perspective of Knowledge Discovery Processes 
7.1.3. Stages of the Knowledge Discovery Processes 
7.1.4. Techniques Used in Knowledge Discovery Processes 
7.1.5. Characteristics of Good Machine Learning Models 
7.1.6. Types of Machine Learning Information 
7.1.7. Basic Learning Concepts 
7.1.8. Basic Concepts of Unsupervised Learning 

7.2. Data Exploration and Pre-Processing 

7.2.1. Data Processing 
7.2.2. Data Processing in the Data Analysis Flow 
7.2.3. Types of Data 
7.2.4. Data Transformations 
7.2.5. Visualization and Exploration of Continuous Variables 
7.2.6. Visualization and Exploration of Categorical Variables 
7.2.7. Correlation Measures 
7.2.8. Most Common Graphic Representations 
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction 

7.3. Decision Trees 

7.3.1. ID Algorithm 
7.3.2. Algorithm C 
7.3.3. Overtraining and Pruning 
7.3.4. Result Analysis 

7.4. Evaluation of Classifiers 

7.4.1. Confusion Matrices 
7.4.2. Numerical Evaluation Matrices 
7.4.3. Kappa Statistic 
7.4.4. ROC Curves 

7.5. Classification Rules 

7.5.1. Rule Evaluation Measures 
7.5.2. Introduction to Graphic Representation 
7.5.3. Sequential Overlay Algorithm 

7.6. Neural Networks 

7.6.1. Basic Concepts 
7.6.2. Simple Neural Networks 
7.6.3. Backpropagation Algorithm 
7.6.4. Introduction to Recurrent Neural Networks 

7.7. Bayesian Methods 

7.7.1. Basic Probability Concepts 
7.7.2. Bayes' Theorem 
7.7.3. Naive Bayes 
7.7.4. Introduction to Bayesian Networks 

7.8. Regression and Continuous Response Models 

7.8.1. Simple Linear Regression 
7.8.2. Multiple Linear Regression 
7.8.3. Logistic Regression 
7.8.4. Regression Trees 
7.8.5. Introduction to Support Vector Machines (SVM) 
7.8.6. Goodness-of-Fit Measures 

7.9. Clustering 

7.9.1. Basic Concepts 
7.9.2. Hierarchical Clustering 
7.9.3. Probabilistic Methods 
7.9.4. EM Algorithm 
7.9.5. B-Cubed Method 
7.9.6. Implicit Methods 

7.10 Text Mining and Natural Language Processing (NLP) 

7.10.1. Basic Concepts 
7.10.2. Corpus Creation 
7.10.3. Descriptive Analysis 
7.10.4. Introduction to Feelings Analysis 

Module 8. Neural Networks, the Basis of Deep Learning 

8.1. Deep Learning 

8.1.1. Types of Deep Learning 
8.1.2. Applications of Deep Learning 
8.1.3. Advantages and Disadvantages of Deep Learning 

8.2. Operations 

8.2.1. Sum 
8.2.2. Product 
8.2.3. Transfer 

8.3. Layers 

8.3.1. Input Layer 
8.3.2. Hidden Layer 
8.3.3. Output Layer 

8.4. Layer Bonding and Operations 

8.4.1. Architecture Design 
8.4.2. Connection between Layers 
8.4.3. Forward Propagation 

8.5. Construction of the First Neural Network 

8.5.1. Network Design 
8.5.2. Establish the Weights 
8.5.3. Network Training 

8.6. Trainer and Optimizer 

8.6.1. Optimizer Selection 
8.6.2. Establishment of a Loss Function 
8.6.3. Establishing a Metric 

8.7. Application of the Principles of Neural Networks 

8.7.1. Activation Functions 
8.7.2. Backward Propagation 
8.7.3. Parameter Adjustment 

8.8. From Biological to Artificial Neurons 

8.8.1. Functioning of a Biological Neuron 
8.8.2. Transfer of Knowledge to Artificial Neurons 
8.8.3. Establish Relations Between the Two 

8.9. Implementation of MLP (Multilayer Perceptron) with Keras 

8.9.1. Definition of the Network Structure 
8.9.2. Model Compilation 
8.9.3. Model Training 

8.10. Fine Tuning Hyperparameters of Neural Networks 

8.10.1. Selection of the Activation Function 
8.10.2. Set the Learning Rate 

8.10. 3. Adjustment of Weights 

Module 9. Deep Neural Networks Training 

9.1. Gradient Problems 

9.1.1. Gradient Optimization Techniques 
9.1.2. Stochastic Gradients 
9.1.3. Weight Initialization Techniques 

9.2. Reuse of Pre-Trained Layers 

9.2.1. Transfer Learning Training 
9.2.2. Feature Extraction 
9.2.3. Deep Learning 

9.3. Optimizers 

9.3.1. Stochastic Gradient Descent Optimizers 
9.3.2. Optimizers Adam and RMSprop 
9.3.3. Moment Optimizers 

9.4. Learning Rate Programming 

9.4.1. Automatic Learning Rate Control 
9.4.2. Learning Cycles 
9.4.3. Smoothing Terms 

9.5. Overfitting 

9.5.1. Cross Validation 
9.5.2. Regularization 
9.5.3. Evaluation Metrics 

9.6. Practical Guidelines 

9.6.1. Model Design 
9.6.2. Selection of Metrics and Evaluation Parameters 
9.6.3. Hypothesis Testing 

9.7. Transfer Learning 

9.7.1. Transfer Learning Training 
9.7.2. Feature Extraction 
9.7.3. Deep Learning 

9.8. Data Augmentation 

9.8.1. Image Transformations 
9.8.2. Synthetic Data Generation 
9.8.3. Text Transformation 

9.9. Practical Application of Transfer Learning 

9.9.1. Transfer Learning Training 
9.9.2. Feature Extraction 
9.9.3. Deep Learning 

9.10. Regularization 

9.10.1. L and L 
9.10.2. Regularization by Maximum Entropy 
9.10.3. Dropout 

Module 10. Model Customization and Training with TensorFlow 

10.1. TensorFlow 

10.1.1. Use of the TensorFlow Library 
10.1.2. Model Training with TensorFlow 
10.1.3. Operations with Graphs in TensorFlow 

10.2. TensorFlow and NumPy 

10.2.1. NumPy Computing Environment for TensorFlow 
10.2.2. Using NumPy Arrays with TensorFlow 
10.2.3. NumPy Operations for TensorFlow Graphs 

10.3. Model Customization and Training Algorithms 

10.3.1. Building Custom Models with TensorFlow 
10.3.2. Management of Training Parameters 
10.3.3. Use of Optimization Techniques for Training 

10.4. TensorFlow Features and Graphs 

10.4.1. Functions with TensorFlow 
10.4.2. Use of Graphs for Model Training 
10.4.3. Graph Optimization with TensorFlow Operations 

10.5. Loading and Preprocessing Data with TensorFlow 

10.5.1. Loading Data Sets with TensorFlow 
10.5.2. Pre-Processing Data with TensorFlow 
10.5.3. Using TensorFlow Tools for Data Manipulation 

10.6. The tf.data API 

10.6.1. Using the tf.data API for Data Processing 
10.6.2. Construction of Data Streams with tf.data 
10.6.3. Using the tfdata API for Model Training 

10.7. The TFRecord Format 

10.7.1. Using the TFRecord API for Data Serialization 
10.7.2. TFRecord File Upload with TensorFlow 
10.7.3. Using TFRecord Files for Model Training 

10.8. Keras Pre-Processing Layers 

10.8.1. Using the Keras Pre-Processing API 
10.8.2. Pre-Processing Pipelined Construction with Keras 
10.8.3. Using the Keras Pre-Processing API for Model Training 

10.9. The TensorFlow Datasets Project 

10.9.1. Using TensorFlow Datasets for Data Loading 
10.9.2. Data Pre-Processing with TensorFlow Datasets 
10.9.3. Using TensorFlow Datasets for Model Training 

10.10. Building a Deep Learning App with TensorFlow 

10.10.1. Practical Applications 
10.10.2. Building a Deep Learning Application with TensorFlow 
10.10.3. Model Training with TensorFlow 
10.10.4. Using the Application for the Prediction of Results 

Module 11. Deep Computer Vision with Convolutional Neural Networks 

11.1. The Visual Cortex Architecture 

11.1.1. Functions of the Visual Cortex 
11.1.2. Theories of Computational Vision 
11.1.3. Models of Image Processing 

11.2. Convolutional Layers 

11.2.1. Reuse of Weights in Convolution 
11.2.2. Convolution D 
11.2.3. Activation Functions 

11.3. Grouping Layers and Implementation of Grouping Layers with Keras 

11.3.1. Pooling and Striding 
11.3.2. Flattening 
11.3.3. Types of Pooling 

11.4. CNN Architecture 

11.4.1. VGG Architecture 
11.4.2. AlexNet Architecture 
11.4.3. ResNet Architecture 

11.5. Implementing a CNN ResNet Using Keras 

11.5.1. Weight Initialization 
11.5.2. Input Layer Definition 
11.5.3. Output Definition 

11.6. Use of Pre-Trained Keras Models 

11.6.1. Characteristics of Pre-Trained Models 
11.6.2. Uses of Pre-Trained Models 
11.6.3. Advantages of Pre-Trained Models 

11.7. Pre-Trained Models for Transfer Learning 

11.7.1. Learning by Transfer 
11.7.2. Transfer Learning Process 
11.7.3. Advantages of Transfer Learning 

11.8. Deep Computer Vision Classification and Localization 

11.8.1. Image Classification 
11.8.2. Localization of Objects in Images 
11.8.3. Object Detection 

11.9. Object Detection and Object Tracking 

11.9.1. Object Detection Methods 
11.9.2. Object Tracking Algorithms 
11.9.3. Tracking and Localization Techniques 

11.10. Semantic Segmentation 

11.10.1. Deep Learning for Semantic Segmentation 
11.10.1. Edge Detection 
11.10.1. Rule-Based Segmentation Methods 

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

12.1. Text Generation Using RNN 

12.1.1. Training an RNN for Text Generation 
12.1.2. Natural Language Generation with RNN 
12.1.3. Text Generation Applications with RNN 

12.2. Training Data Set Creation 

12.2.1. Preparation of the Data for Training an RNN 
12.2.2. Storage of the Training Dataset 
12.2.3. Data Cleaning and Transformation 
12.2.4. Sentiment Analysis 

12.3. Classification of Opinions with RNN 

12.3.1. Detection of Themes in Comments 
12.3.2. Sentiment Analysis with Deep Learning Algorithms 

12.4. Encoder-Decoder Network for Neural Machine Translation 

12.4.1. Training an RNN for Machine Translation 
12.4.2. Use of an Encoder-Decoder Network for Machine Translation 
12.4.3. Improving the Accuracy of Machine Translation with RNNs 

12.5. Attention Mechanisms 

12.5.1. Application of Care Mechanisms in RNN 
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models 
12.5.3. Advantages of Attention Mechanisms in Neural Networks 

12.6. Transformer Models 

12.6.1. Using Transformers Models for Natural Language Processing 
12.6.2. Application of Transformers Models for Vision 
12.6.3. Advantages of Transformers Models 

12.7. Transformers for Vision 

12.7.1. Use of Transformers Models for Vision 
12.7.2. Image Data Pre-Processing 
12.7.3. Training a Transformers Model for Vision 

12.8. Hugging Face’s Transformers Library 

12.8.1. Using Hugging Face's Transformers Library 
12.8.2. Hugging Face’s Transformers Library Application 
12.8.3. Advantages of Hugging Face’s Transformers Library 

12.9. Other Transformers Libraries. Comparison 

12.9.1. Comparison Between Different Transformers Libraries 
12.9.2. Use of the Other Transformers Libraries 
12.9.3. Advantages of the Other Transformers Libraries 

12.10. Development of an NLP Application with RNN and Attention. Practical Applications 

12.10.1. Development of a Natural Language Processing Application with RNN and Attention 
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application 
12.10.3. Evaluation of the Practical Application 

Module 13. Autoencoders, GANs and Diffusion Models 

13.1. Representation of Efficient Data 

13.1.1. Dimensionality Reduction 
13.1.2. Deep Learning 
13.1.3. Compact Representations 

13.2. PCA Realization with an Incomplete Linear Automatic Encoder 

13.2.1. Training Process 
13.2.2. Implementation in Python 
13.2.3. Use of Test Data 

13.3. Stacked Automatic Encoders 

13.3.1. Deep Neural Networks 
13.3.2. Construction of Coding Architectures 
13.3.3. Use of Regularization 

13.4. Convolutional Autoencoders 

13.4.1. Design of Convolutional Models 
13.4.2. Convolutional Model Training 
13.4.3. Results Evaluation 

13.5. Noise Suppression of Automatic Encoders 

13.5.1. Filter Application 
13.5.2. Design of Coding Models 
13.5.3. Use of Regularization Techniques 

13.6. Sparse Automatic Encoders 

13.6.1. Increasing Coding Efficiency 
13.6.2. Minimizing the Number of Parameters 
13.6.3. Using Regularization Techniques 

13.7. Variational Automatic Encoders 

13.7.1. Use of Variational Optimization 
13.7.2. Unsupervised Deep Learning 
13.7.3. Deep Latent Representations 

13.8. Generation of Fashion MNIST Images 

13.8.1. Pattern Recognition 
13.8.2. Image Generation 
13.8.3. Deep Neural Networks Training 

13.9. Generative Adversarial Networks and Diffusion Models 

13.9.1. Content Generation from Images 
13.9.2. Modeling of Data Distributions 
13.9.3. Use of Adversarial Networks 

13.10. Implementation of the Models 

13.10.1. Practical Application 
13.10.2. Implementation of the Models 
13.10.3. Use of Real Data 
13.10.4. Results Evaluation 

Module 14. Bio-Inspired Computing 

14.1. Introduction to Bio-Inspired Computing 

14.1.1. Introduction to Bio-Inspired Computing 

14.2. Social Adaptation Algorithms 

14.2.1. Bio-Inspired Computation Based on Ant Colonies 
14.2.2. Variants of Ant Colony Algorithms 
14.2.3. Particle Cloud Computing 

14.3. Genetic Algorithms 

14.3.1. General Structure 
14.3.2. Implementations of the Major Operators 

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms 

14.4.1. CHC Algorithm 
14.4.2. Multimodal Problems 

14.5. Evolutionary Computing Models (I) 

14.5.1. Evolutionary Strategies 
14.5.2. Evolutionary Programming 
14.5.3. Algorithms Based on Differential Evolution 

14.6. Evolutionary Computation Models (II) 

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA) 
14.6.2. Genetic Programming 

14.7. Evolutionary Programming Applied to Learning Problems 

14.7.1. Rules-Based Learning 
14.7.2. Evolutionary Methods in Instance Selection Problems 

14.8. Multi-Objective Problems 

14.8.1. Concept of Dominance 
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems 

14.9. Neural Networks (I) 

14.9.1. Introduction to Neural Networks 
14.9.2. Practical Example with Neural Networks 

14.10. Neural Networks (II) 

14.10.1. Use Cases of Neural Networks in Medical Research 
14.10.2. Use Cases of Neural Networks in Economics 
14.10.3. Use Cases of Neural Networks in Artificial Vision 

Module 15. Artificial Intelligence: Strategies and Applications 

15.1. Financial Services 

15.1.1. The Implications of Artificial Intelligence (AI) in Financial Services. Opportunities and Challenges 
15.1.2. Case Studies 
15.1.3. Potential Risks Related to the Use of AI 
15.1.4. Potential Future Developments/Uses of AI 

15.2. Implications of Artificial Intelligence in Healthcare Service 

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges 
15.2.2. Case Studies 

15.3. Risks Related to the Use of AI in Healthcare Service 

15.3.1. Potential Risks Related to the Use of AI 
15.3.2. Potential Future Developments/Uses of AI 

15.4. Retail 

15.4.1. Implications of AI in Retail. Opportunities and Challenges 
15.4.2. Case Studies 
15.4.3. Potential Risks Related to the Use of AI 
15.4.4. Potential Future Developments/Uses of AI 

15.5. Industry 

15.5.1. Implications of AI in Industry. Opportunities and Challenges 
15.5.2. Case Studies 

15.6. Potential Risks Related to the Use of AI in Industry 

15.6.1. Case Studies 
15.6.2. Potential Risks Related to the Use of AI 
15.6.3. Potential Future Developments/Uses of AI 

15.7. Public Administration 

15.7.1. AI Implications for Public Administration. Opportunities and Challenges 
15.7.2. Case Studies 
15.7.3. Potential Risks Related to the Use of AI 
15.7.4. Potential Future Developments/Uses of AI 

15.8. Educational 

15.8.1. AI Implications for Education. Opportunities and Challenges 
15.8.2. Case Studies 
15.8.3. Potential Risks Related to the Use of AI 
15.8.4. Potential Future Developments/Uses of AI 

15.9. Forestry and Agriculture 

15.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges 
15.9.2. Case Studies 
15.9.3. Potential Risks Related to the Use of AI 
15.9.4. Potential Future Developments/Uses of AI 

15.10. Human Resources 

15.10.1. Implications of AI in Human Resources. Opportunities and Challenges 
15.10.2. Case Studies 
15.10.3. Potential Risks Related to the Use of AI 
15.10.4. Potential Future Developments/Uses of AI 

Module 16. Practical Applications of Artificial Intelligence in Design 

16.1. Automatic Image Generation in Graphic Design with Wall-e, Adobe Firefly and Stable Diffusion 

16.1.1. Fundamental Concepts of Image Generation 
16.1.2. Tools and Frameworks for Automatic Graphic Generation 
16.1.3. Social and Cultural Impact of Generative Design 
16.1.4. Current Trends in the Field and Future Developments and Applications

16.2. Dynamic Personalization of User Interfaces Using AI 

16.2.1. UI/UX Personalization Principles 
16.2.2. Recommendation Algorithms in UI Customization 
16.2.3. User Experience and Continuous Feedback 
16.2.4. Practical Implementation in Real Applications 

16.3. Generative Design: Applications in Industry and Art 

16.3.1. Fundamentals of Generative Design 
16.3.2. Generative Design in Industry 
16.3.3. Generative Design in Contemporary Art 
16.3.4. Challenges and Future Advances in Generative Design 

16.4. Automatic Creation of Editorial Layouts with Algorithms 

16.4.1. Principles of Automatic Editorial Layout 
16.4.2. Content Distribution Algorithms 
16.4.3. Optimization of Spaces and Proportions in Editorial Design 
16.4.4. Automation of the Review and Adjustment Process 

16.5. Procedural Generation of Content in Videogames with PCG 

16.5.1. Introduction to Procedural Generation in Videogames 
16.5.2. Algorithms for the Automatic Creation of Levels and Environments 
16.5.3. Procedural Narrative and Branching in Videogames 
16.5.4. Impact of Procedural Generation on the Player's Experience 

16.6. Pattern Recognition in Logos with Machine Learning Using Cogniac 

16.6.1. Fundamentals of Pattern Recognition in Graphic Design 
16.6.2. Implementation of Machine Learning Models for Logo Identification 
16.6.3. Practical Applications in Graphic Design 

16.6. Legal and Ethical Considerations in the Recognition of Logos 
16.7. Optimization of Colors and Compositions with AI 

16.7.1. Color Psychology and Visual Composition 
16.7.2. Color Optimization Algorithms in Graphic Design with Adobe Color Wheel and Coolors 
16.7.3. Automatic Composition of Visual Elements using Framer, Canva and RunwayML 
16.7.4. Evaluating the Impact of Automatic Optimization on User Perception 

16.8. Predictive Analysis of Visual Trends in Design 

16.8.1. Data Collection and Current Trends 
16.8.2. Machine Learning Models for Trend Prediction 
16.8.3. Implementation of Proactive Design Strategies 
16.8.4. Principles in the Use of Data and Predictions in Design 

16.9. AI-assisted Collaboration in Design Teams 

16.9.1. Human-AI Collaboration in Design Projects 
16.9.2. Platforms and Tools for AI-assisted Collaboration (Adobe Creative Cloud and Sketch2React) 
16.9.3. Best Practices in AI-assisted Technology Integration 
16.9.4. Future Perspectives on Human-AI Collaboration in Design 

16.10. Strategies for the Successful Incorporation of AI in Design 

16.10.1. Identification of AI-solvable Design Needs 
16.10.2. Evaluation of Available Platforms and Tools 
16.10.3. Effective Integration in Design Projects 
16.10.4. Continuous Optimization and Adaptability 

Module 17. Design-User Interaction and AI 

17.1. Contextual Suggestions for Behavior-Based Design 

17.1.1. Understanding User Behavior in Design 
17.1.2. AI-based Contextual Suggestion Systems 
17.1.3. Strategies to Ensure Transparency and User Consent 
17.1.4. Trends and Possible Improvements in Behavior-based Personalization 

17.2. Predictive Analysis of User Interactions 

17.2.1. Importance of Predictive Analytics in User-Design Interactions 
17.2.2. Machine Learning Models for Predicting User Behavior 
17.2.3. Integration of Predictive Analytics in User Interface Design 
17.2.4. Challenges and Dilemmas in Predictive Analytics 

17.3. Adaptive Design to Different Devices with AI 

17.3.1. Principles of Device Adaptive Design 
17.3.2. Content Adaptation Algorithms 
17.3.3. Interface Optimization for Mobile and Desktop Experiences 
17.3.4. Future Developments in Adaptive Design with Emerging Technologies 

17.4. Automatic Generation of Characters and Enemies in Video Games 

17.4.1. The Need for Automatic Generation in the Development of Video Games 
17.4.2. Algorithms for Character and Enemy Generation 
17.4.3. Customization and Adaptability in Automatically Generated Characters 
17.4.4. Development Experiences: Challenges and Lessons Learned 

17.5. AI Improvement in Game Characters 

17.5.1. Importance of Artificial Intelligence in Video Game Characters 
17.5.2. Algorithms to Improve the Behavior of Characters 
17.5.3. Continuous Adaptation and Learning of AI in Games 
17.5.4. Technical and Creative Challenges in Character AI Improvement 

17.6. Custom Design in Industry: Challenges and Opportunities 

17.6.1. Transformation of Industrial Design with Personalization 
17.6.2. Enabling Technologies for Customized Design 
17.6.3. Challenges in Implementing Customized Design at Scale 
17.6.4. Opportunities for Innovation and Competitive Differentiation 

17.7. Design for Sustainability Through AI 

17.7.1. Life Cycle Analysis and Traceability with Artificial Intelligence 
17.7.2. Optimization of Recyclable Materials 
17.7.3. Improvement of Sustainable Processes 
17.7.4. Development of Practical Strategies and Projects 

17.8. Integration of Virtual Assistants in Design Interfaces with Adobe Sensei, Figma and AutoCAD 

17.8.1. Role of Virtual Assistants in Interactive Design 
17.8.2. Development of Virtual Assistants Specialized in Design 
17.8.3. Natural Interaction with Virtual Assistants in Design Projects 
17.8.4. Implementation Challenges and Continuous Improvement 

17.9. Continuous User Experience Analysis for Improvement 

17.9.1. Continuous Improvement Cycle in Interaction Design 
17.9.2. Tools and Metrics for Continuous Analysis 
17.9.3. Iteration and Adaptation in User Experience 
17.9.4. Ensuring Privacy and Transparency in the Handling of Sensitive Data 

17.10. Application of AI Techniques to Improve Usability 

17.10.1. Intersection of AI and Usability 
17.10.2. Sentiment and User Experience (UX) Analysis 
17.10.3. Dynamic Interface Personalization 
17.10.4. Workflow and Navigation Optimization 

Module 18. Innovation in Design and AI Processes 

18.1. Optimization of Manufacturing Processes with AI Simulations 

18.1.1. Introduction to Manufacturing Process Optimization 
18.1.2. AI Simulations for Production Optimization 
18.1.3. Technical and Operational Challenges in the Implementation of AI Simulations 
18.1.4. Future Perspectives: Advances in Process Optimization with AI 

18.2. Virtual Prototyping: Challenges and Benefits 

18.2.1. Importance of Virtual Prototyping in Design 
18.2.2. Tools and Technologies for Virtual Prototyping 
18.2.3. Challenges in Virtual Prototyping and Strategies for Overcoming Them 
18.2.4. Impact on Design Innovation and Agility 

18.3. Generative Design: Applications in Industry and Artistic Creation 

18.3.1. Architecture and Urban Planning 
18.3.2. Fashion and Textile Design 
18.3.3. Design of Materials and Textures 
18.3.4. Automation in Graphic Design 

18.4. Materials and Performance Analysis Using Artificial Intelligence 

18.4.1. Importance of Materials and Performance Analysis in Design 
18.4.2. Artificial Intelligence Algorithms for Material Analysis 
18.4.3. Impact on Design Efficiency and Sustainability 
18.4.4. Implementation Challenges and Future Applications 

18.5. Mass Customization in Industrial Production 

18.5.1. Transformation of Production Through Mass Customization 
18.5.2. Enabling Technologies for Mass Customization 
18.5.3. Logistical and Scale Challenges of Mass Customization 
18.5.4. Economic Impact and Innovation Opportunities 

18.6. Artificial Intelligence Fotor Assisted Design Tools Fotor and Snappa) 

18.6.1. Generation-Assisted Design Gan (Generative Adversarial Networks) 
18.6.2. Collective Generation of Ideas 
18.6.3. Context-aware Generation 
18.6.4. Exploration of Non-linear Creative Dimensions 

18.7. Collaborative Human-robot Design in Innovative Projects 

18.7.1. Integration of Robots in Innovative Design Projects 
18.7.2. Tools and Platforms for Human-robot Collaboration (ROS, OpenAI Gym and Azure Robotics) 
18.7.3. Challenges in Integrating Robots in Creative Projects 
18.7.4. Future Perspectives in Collaborative Design with Emerging Technologies 

18.8. Predictive Maintenance of Products: AI Approach 

18.8.1. Importance of Predictive Maintenance in Product Prolongation 
18.8.2. Machine Learning Models for Predictive Maintenance 
18.8.3. Practical Implementation in Various Industries 
18.8.4. Evaluation of the Accuracy and Effectiveness of these Models in Industrial Environments 

18.9. Automatic Generation of Typefaces and Visual Styles 

18.9.1. Fundamentals of Automatic Generation in Typeface Design 
18.9.2. Practical Applications in Graphic Design and Visual Communication 
18.9.3. AI-assisted Collaborative Design in the Creation of Typefaces 
18.9.4. Exploration of Automatic Styles and Trends 

18.10. IoT Integration for Real-time Product Monitoring 

18.10.1. Transformation with the Integration of IoT in Product Design 
18.10.2. Sensors and IoT Devices for Real Time Monitoring 
18.10.3. Data Analysis and IoT-based Decision Making 
18.10.4. Implementation Challenges and Future Applications of IoT in Design 

Module 19. Applied Design Technologies and AI 

19.1. Integration of Virtual Assistants in Design Interfaces with Dialogflow, Microsoft Bot Framework and Rasa 

19.1.1. Role of Virtual Assistants in Interactive Design 
19.1.2. Development of Virtual Assistants Specialized in Design 
19.1.3. Natural Interaction with Virtual Assistants in Design Projects 
19.1.4. Implementation Challenges and Continuous Improvement 

19.2. Automatic Detection and Correction of Visual Errors with AI 

19.2.1. Importance of Automatic Visual Error Detection and Correction 
19.2.2. Algorithms and Models for Visual Error Detection 
19.2.3. Automatic Correction Tools in Visual Design 
19.2.4. Challenges in Automatic Detection and Correction and Strategies for Overcoming Them 

19.3. AI Tools for Usability Evaluation of Interface Designs (EyeQuant, Lookback and Mouseflow) 

19.3.1. Analysis of Interaction Data with Machine Learning Models 
19.3.2. Automated Report Generation and Recommendations 
19.3.3. Virtual User Simulations for Usability Testing Using Bootpress, Botium and Rasa 
19.3.4. Conversational Interface for User Feedback 

19.4. Optimization of Editorial Workflows with GPT Chat, Bing, WriteSonic and Jasper Algorithms 

19.4.1. Importance of Optimizing Editorial Workflows 
19.4.2. Algorithms for Editorial Automation and Optimization 
19.4.3. Tools and Technologies for Editorial Optimization 
19.4.4. Challenges in Implementation and Continuous Improvement in Editorial Workflows 

19.5. Realistic Simulations in Video Game Design with TextureLab and Leonardo 

19.5.1. Importance of Realistic Simulations in the Videogame Industry 
19.5.2. Modeling and Simulation of Realistic Elements in Video Games 
19.5.3. Technologies and Tools for Realistic Simulations in Video Games 
19.5.4. Technical and Creative Challenges in Realistic Video Game Simulations 

19.6. Automatic Generation of Multimedia Content in Editorial Design 

19.6.1. Transformation with Automatic Generation of Multimedia Content 
19.6.2. Algorithms and Models for the Automatic Generation of Multimedia Content 
19.6.3. Practical Applications in Publishing Projects 
19.6.4. Challenges and Future Trends in the Automatic Generation of Multimedia Content 

19.7. Adaptive and Predictive Design Based on User Data 

19.7.1. Importance of Adaptive and Predictive Design in User Experience 
19.7.2. Collection and Analysis of User Data for Adaptive Design 
19.7.3. Algorithms for Adaptive and Predictive Design 
19.7.4. Integration of Adaptive Design in Platforms and Applications 

19.8. Integration of Algorithms in Usability Improvement 

19.8.1. Segmentation and Behavioral Patterns 
19.8.2. Detection of Usability Problems 
19.8.3. Adaptability to Changes in User Preferences 
19.8.4. Automated a/b Testing and Analysis of Results 

19.9. Continuous Analysis of User Experience for Iterative Improvements 

19.9.1. Importance of Continuous Feedback in Product and Service Evolution 
19.9.2. Tools and Metrics for Continuous Analysis 
19.9.3. Case Studies Demonstrating Substantial Improvements Achieved Through this Approach 
19.9.4. Handling of Sensitive Data 

19.10. AI-assisted Collaboration in Editorial Teams 

19.10.1. Transforming Collaboration in AI-assisted Editorial Teams 
19.10.2. Tools and Platforms for AI-Assisted Collaboration (Grammarly, Yoast SEO and Quillionz) 
19.10.3. Development of Virtual Assistants Specialized in Editing 
19.10.4. Implementation Challenges and Future Applications of AI-assisted Collaboration 

Module 20. Ethics and Environment in Design and AI 

20.1. Environmental Impact in Industrial Design: Ethical Approach 

20.1.1. Environmental Awareness in Industrial Design 
20.1.2. Life Cycle Assessment and Sustainable Design 
20.1.3. Ethical Challenges in Design Decisions with Environmental Impact 
20.1.4. Sustainable Innovations and Future Trends 

20.2. Improving Visual Accessibility in Responsive Graphic Design 

20.2.1. Visual Accessibility as an Ethical Priority in Graphic Design 
20.2.2. Tools and Practices for Improving Visual Accessibility (Google LightHouse and Microsoft Accessibility Insights) 
20.2.3. Ethical Challenges in Implementing Visual Accessibility 
20.2.4. Professional Responsibility and Future Improvements in Visual Accessibility 

20.3. Waste Reduction in the Design Process: Sustainable Challenges 

20.3.1. Importance of Waste Reduction in Design 
20.3.2. Strategies for Waste Reduction at Different Stages of Design 
20.3.3. Ethical Challenges in Implementing Waste Reduction Practices 
20.3.4. Corporate Commitments and Sustainable Certifications 

20.4. Sentiment Analysis in Editorial Content Creation: Ethical Considerations 

20.4.1. Sentiment Analysis and Ethics in Editorial Content 
20.4.2. Algorithms for Sentiment Analysis and Ethical Decisions 
20.4.3. Impact on Public Opinion 
20.4.4. Challenges in Sentiment Analysis and Future Implications 

20.5. Integration of Emotion Recognition for Immersive Experiences 

20.5.1. Ethics in the Integration of Emotion Recognition in Immersive Experiences 
20.5.2. Emotion Recognition Technologies 
20.5.3. Ethical Challenges in Creating Emotionally Aware Immersive Experiences 
20.5.4. Future Perspectives and Ethics in the Development of Immersive Experiences 

20.6. Ethics in Video Game Design: Implications and Decisions 

20.6.1. Ethics and Responsibility in Videogame Design 
20.6.2. Inclusion and Diversity in Video Games: Ethical Decisions 
20.6.3. Microtransactions and Ethical Monetization in Videogames 
20.6.4. Ethical Challenges in the Development of Narratives and Characters in Videogames 

20.7. Responsible Design: Ethical and Environmental Considerations in the Industry 

20.7.1. Ethical Approach to Responsible Design 
20.7.2. Tools and Methods for Responsible Design 
20.7.3. Ethical and Environmental Challenges in the Design Industry 
20.7.4. Corporate Commitments and Responsible Design Certifications 

20.8. Ethics in the Integration of AI in User Interfaces 

20.8.1. Exploration of How Artificial Intelligence in User Interfaces Raises Ethical Challenges 
20.8.2. Transparency and Explainability in AI Systems in User Interfaces 
20.8.3. Ethical Challenges in the Collection and Use of User Interface Data 
20.8.4. Future Perspectives on AI Ethics at User Interfaces 

20.9. Sustainability in Design Process Innovation 

20.9.1. Recognition of the Importance of Sustainability in Design Process Innovation 
20.9.2. Development of Sustainable Processes and Ethical Decision-Making 
20.9.3. Ethical Challenges in the Adoption of Innovative Technologies 
20.9.4. Business Commitments and Sustainability Certifications in Design Processes 

20.10. Ethical Aspects in the Application of Design Technologies 

20.10.1. Ethical Decisions in the Selection and Application of Design Technologies 
20.10.2. Ethics in the Design of User Experiences with Advanced Technologies 
20.10.3. Intersections of Ethics and Technologies in Design 
20.10.4. Emerging Trends and the Role of Ethics in the Future Direction of Design with Advanced Technologies

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