Thanks to this Hybrid professional master’s degree, you will integrate intelligent algorithms into your Design work, accessing deeper data analysis, automating repetitive tasks, and generating innovative solutions"

Artificial Intelligence (AI) offers designers a wide range of tools and capabilities to boost their creativity and efficiency. From automatic design generation to process optimization, AI provides the opportunity to explore new frontiers and innovative solutions. It also facilitates customization and rapid adaptation to changing market needs, allowing designers to focus on creative expression and the creation of meaningful experiences for users.

This is how this Hybrid professional master’s degree was created, in which designers will apply collaborative tools powered by AI, improving communication and efficiency in design teams. In addition, they will analyze how to incorporate emotional aspects in designs using techniques that effectively connect with the audience, and how AI can influence the emotional perception of Design.

Likewise, the interaction between Design and user through AI will be deepened, developing skills in adaptive design and critically analyzing the challenges and opportunities when implementing personalized designs. Predictive algorithms will also be used to anticipate user interactions and develop AI-based recommender systems, enabling the creation of more personalized and efficient user experiences.

Finally, innovation in design processes through Artificial Intelligence will be addressed, from mass customization of products to the application of techniques to minimize waste and foster creativity in design. Likewise, professionals will acquire practical skills to use AI as a tool to generate innovative and sustainable solutions.
Therefore, this Hybrid professional master’s degree will include an internship in a prestigious international company. During 3 weeks, the professionals will join a multidisciplinary work team to carry out tasks related to creative and design projects. It should be noted that, during this stage, they will be accompanied by a specialized tutor, who will reinforce the mastery of the contents by means of the use of the most avant-garde tools in this area.

You will integrate Artificial Intelligence into your designs, leveraging tangible benefits to drive innovation and excellence in your profession. What are you waiting for to enroll?"

This Hybrid 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:

• Development of more than 100 case studies presented by design professionals specialized in the use of Artificial Intelligence and university professors with extensive experience in the field
• Its graphic, schematic and practical contents provide essential information on those disciplines that are indispensable for professional practice
• All of this will be complemented by 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
• Furthermore, you will be able to carry out an internship in one of the best companies

You will carry out an intensive practical internship, lasting 3 weeks, in a prestigious institution, so that you acquire the knowledge and skills essential to grow personally and professionally"

In this Hybrid professional master’s degree proposal, of a  professionalizing character and blended learning modality, the program is aimed at updating Design professionals who develop their activity using Artificial Intelligence tools, which require a high level of qualification. The contents are based on the latest scientific evidence, and oriented in a didactic way to integrate the theoretical knowledge in the practice of Artificial Intelligence in Design, and the theoretical-practical elements will facilitate the updating of knowledge and will allow decision making in patient management.

Thanks to its multimedia content elaborated with the latest educational technology, they will allow the design professional a situated and contextual learning, that is, a simulated environment that will provide an immersive learning programmed to specialize in real situations. This program is designed around Problem-Based Learning, whereby the physician must try to solve the different professional practice situations that arise during the course. For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will master specific tools, such as Generative Adversarial Networks (GANs), essential to automate the generation of visual elements and optimize creative processes"

Through this university program, you will be prepared to face the challenges and take advantage of the opportunities offered by AI in the field of Design, always maintaining an ethical and responsible approach"

This curriculum is made up of 20 specialized modules, which will equip designers with the skills required to handle Artificial Intelligence tools and use them in their design processes. To this end, the syllabus will delve into essential issues, including Data Mining, Machine Learning, Neural Networks or Model Personalization and TensorFlow Training. In this way, graduates will implement these technological tools in their projects for tasks such as the personalization of the user experience.

You will master programming languages, such as TensorFlow, to deploy Artificial Intelligence models in Design environments. With all the quality guarantees that characterize TECH!"

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-Based 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 Personalities: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Data 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 their 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. Data Warehouse

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/Safety

2.10. Regulatory Aspects BORRAR

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Normative 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. Merge Sort
5.3.6. Quick Sorting (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. Analysis of Results

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
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. Surgery

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Cloak
8.3.3. Output Layer

8.4. Union of Layers 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. Learning Transfer 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. Learning Transfer 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. Learning Transfer 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. Optimization of Graphs with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading of Datasets with TensorFlow
10.5.2. Preprocessing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tf.data API

10.6.1. Using the tf.dataAPI for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data 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 Preprocessing Layers

10.8.1. Using the Keras Preprocessing API
10.8.2. Preprocessing Pipelined Construction with Keras
10.8.3. Using the Keras Preprocessing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Preprocessing Data 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 Application
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Use of 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. Architecture ResNet

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. Transfer Learning
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 Preprocessing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s Transformers Library

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

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. Automatic Encoder Denoising

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 Uses
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 the Healthcare Service

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

15.3. Risks Related to the Use of AI in the Health 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 Uses
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 Uses

15.6. Potential risks related to the use of AI in industry

15.6.1. Case Uses
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 Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/uses of AI

15.8. Education

15.8.1. AI implications for education. Opportunities and Challenges
15.8.2. Case Uses
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 Uses
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 for Human Resources Opportunities and Challenges
15.10.2. Case Uses
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 Interface Personalization 
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 Revision and Adjustment Process 

16.5. Procedural Generation of Content in Videogames with PCG

16.5.1. Introduction to Procedural Generation in Video Games 
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 
16.6.4. Legal and Ethical Considerations in Logo Recognition 

16.7. Color and Composition Optimization 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's 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 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 Behavioral-Based Design Suggestions 

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 Behavioral 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 User Behavior Prediction 
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 Character and Enemy Generation in Video Game Development 
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 for Improving Character Behavior 
17.5.3. Continuous Adaptation and Learning of AI in Games 
17.5.4. Technical and Creative Challenges in Character AI Enhancement 

17.6. Custom Design in the Industry: Challenges and Opportunities 

17.6.1. Transformation of Industrial Design with Customization 
17.6.2. Enabling Technologies for Customized Design 
17.6.3. Challenges in Implementing Customized Design to 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 Handling Sensitive Data 

17.10. Application of AI Techniques for Usability Enhancement 

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 Implementing AI Simulations 
18.1.4. Future Perspectives: Advances in Process Optimization with AI 

18.2. Virtual Prototypes Creation: 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: Applicability 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-Assisted Design Tools. (Fotor and Snappa) 

18.6.1. Gan-Generation Aided Design (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. Human-Robot Collaborative 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 Extending Product Lifetime 
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 Typographies and Visual Styles 

18.9.1. Fundamentals of Automatic Typeface 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 to Monitor Products in Real-Time 

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 Analytics 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 and AutoCAD 

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 with Bootpress, Botium and Rasa 
19.3.4. Conversational Interface for User Feedback 

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

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 Video Game Industry 
19.5.2. Modeling and Simulation of Realistic Elements in Videogames 
19.5.3. Technologies and Tools for Realistic Simulations in Videogames 
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 Multimedia Content Generation 
19.6.2. Algorithms and Models for Automatic Multimedia Content Generation 
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 for Improving Usability 

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 by This Approach 
19.9.4. Handling of Sensitive Data 

19.10. AI-Assisted Collaboration in Editorial Teams 

19.10.1. Transformation of AI-Assisted Collaboration in 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 Edition  
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 Graphic Design with Responsibility 

20.2.1. Visual Accessibility as an Ethical Priority in Graphic Design 
20.2.2. Tools and Practices for Visual Accessibility Improvement (Google LightHouse and Microsoft Accessibility Insights) 
20.2.3. Ethical Challenges in Visual Accessibility Implementation 
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 into 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 Video Game 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 Certifications for Responsible Design 

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 in User Interfaces 

20.9. Sustainability in Design Process Innovation 

20.9.1. Recognition of the Importance of Sustainability in AI in User Interfaces 
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 

This program will enable you to develop Artificial Intelligence models using Computer Vision techniques, to address specific problems in Design"