Take the opportunity to learn about the latest advances in this field in order to apply it to your daily practice"

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills education.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success”

At TECH Global University

Innovation

The university offers an online learning model that balances the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system. 

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives prepared each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent. 

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea. 

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning methodology (the most internationally recognized postgraduate learning methodology) with Harvard Business School case studies. A complex balance of traditional and state-of-the-art methods, within the most demanding academic framework.   

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

Through 30 modules, graduates will gain the skills required to effectively integrate Artificial Intelligence into all stages of the design process. The syllabus will examine issues ranging from Data Science or Algorithmics to Machine Learning. Likewise, the syllabus will delve into the construction of Neural Networks, which will help graduates to solve complex problems of data analysis or image processing. In addition, the didactic materials will delve into Bio-inspired Computing, offering students innovative techniques such as Generic Algorithms or Ant Colonies.

A high-level syllabus that covers the latest scientific postulates in Deep Neural Network Training”

Syllabus

The MBA in Artificial Intelligence in Design at TECH Global University is an intense program that prepares students to face challenges and business decisions both nationally and internationally. Its content is designed to promote the development of managerial skills that enable more rigorous decision-making in uncertain environments. 

Throughout 3,600 hours of study, students will analyze a multitude of practical cases through individual work, achieving high quality learning that can be applied to their daily practice. It is, therefore, an authentic immersion in real business situations.

This program deals in depth with the main areas of the Artificial Intelligence and is designed for designers to understand their applications from a strategic, international and innovative perspective.

A plan designed for students, focused on their professional improvement and preparing them to achieve excellence in the field of Design. A program that understands their needs and those of their company through innovative content based on the latest trends, and supported by the best educational methodology and an exceptional faculty, which will provide them with the skills to solve critical situations in a creative and efficient way.

Module 1. Leadership, Ethics and Social Responsibility in Companies
Module 2. Strategic Managementand Executive Management 
Module 3. People and Talent Management
Module 4. Economic and Financial Management
Module 5. Operations and Logistics Management
Module 6. Information Systems Management
Module 7. Commercial Management, Strategic Marketing and Corporate Communications
Module 8. Market Research, Advertising and Commercial Management
Module 9 Innovation and Project Management
Module 10. Executive Management
Module 11. Fundamentals of Artificial Intelligence
Module 12. Data Types and Life Cycle
Module 13. Data in Artificial Intelligence
Module 14. Data Mining: Selection, Pre-Processing and Transformation
Module 15. Algorithm and Complexity in Artificial Intelligence
Module 16. Intelligent Systems
Module 17. Machine Learning and Data Mining
Module 18. Neural Networks, the Basis of Deep Learning
Module 19. Deep Neural Networks Training
Module 21. Model Customization and Training with
Module 22. Deep Computer Vision with Convolutional Neural Networks
Module 23. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention
Module 24. Autoencoders, GANs and Diffusion Models
Module 25. Bio-Inspired Computing
Module 26. Artificial Intelligence: Strategies and Applications
Module 27. Design-User Interaction and AI
Module 28. Innovation in Design and AI Processes 
Module 29. Applied Design Technologies and AI 
Module 30. Ethics and Environment in Design and AI

Where, When and How is it Taught?

TECH offers the possibility of developing this MBA in Artificial Intelligence in Design completely online. Throughout the 24 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their study time. 

Module 1. Leadership, Ethics and Social Responsibility in Companies

1.1. Globalization and Governance

1.1.1. Governance and Corporate Governance
1.1.2. The Fundamentals of Corporate Governance in Companies
1.1.3. The Role of the Board of Directors in the Corporate Governance Framework

1.2. Leadership

1.2.1. Leadership. A Conceptual Approach
1.2.2. Leadership in Companies
1.2.3. The Importance of Leaders in Business Management

1.3. Cross Cultural Management

1.3.1. Cross Cultural Management Concept
1.3.2. Contributions to Knowledgeof National Cultures
1.3.3. Diversity Management

1.4. Management and Leadership Development

1.4.1. Concept of Management Development
1.4.2. Concept of Leadership
1.4.3. Leadership Theories
1.4.4. Leadership Styles
1.4.5. Intelligence in Leadership
1.4.6. The Challenges of Today's Leader

1.5. Business Ethics

1.5.1. Ethics and Morality
1.5.2. Business Ethics
1.5.3. Leadership and Ethics in Companies

1.6. Sustainability

1.6.1. Sustainability and Sustainable Development
1.6.2. The 2030 Agenda
1.6.3. Sustainable Companies

1.7. Corporate Social Responsibility

1.7.1. International Dimensions of Corporate Social Responsibility
1.7.2. Implementing Corporate Social Responsibility
1.7.3. The Impact and Measurement of Corporate Social Responsibility

1.8. Responsible Management Systems and Tools

1.8.1. CSR: Corporate Social Responsibility
1.8.2. Essential Aspects for Implementing a Responsible Management Strategy
1.8.3. Steps for the Implementation of a Corporate Social Responsibility Management System
1.8.4. CSR Tools and Standards

1.9. Multinationals and Human Rights

1.9.1. Globalization, Multinational Companies and Human Rights
1.9.2. Multinational Companies vs.International Law
1.9.3. Legal Instruments for Multinationals in the Area of Human Rights

1.10. Legal Environment and

1.10.1. International Rules on Importation and Exportation
1.10.2. Intellectual and Industrial Property
1.10.3. International Labor Law

Module 2. Strategic Management and Executive Management 

2.1. Organizational Analysis and Design

2.1.1. Conceptual Framework
2.1.2. Key Elements in Organizational Design
2.1.3. Basic Organizational Models
2.1.4. Organizational Design: Typologies

2.2. Corporate Strategy

2.2.1. Competitive Corporate Strategy
2.2.2. Types of Growth Strategies
2.2.3. Conceptual Framework

2.3. Strategic Planning and Strategy Formulation

2.3.1. Conceptual Framework
2.3.2. Elements of Strategic Planning
2.3.3. Strategy Formulation: Strategic Planning Process

2.4. Strategic Thinking

2.4.1. The Company as a System
2.4.2. Organization Concept

2.5. Financial Diagnosis

2.5.1. Concept of Financial Diagnosis
2.5.2. Stages of Financial Diagnosis
2.5.3. Assessment Methods for Financial Diagnosis

2.6. Planning and Strategy

2.6.1. The Plan from a Strategy
2.6.2. Strategic Positioning
2.6.3. Strategy in Companies

2.7. Strategy Models and Patterns

2.7.1. Conceptual Framework
2.7.2. Strategic Models
2.7.3. Strategic Patterns: The Five P’s of Strategy

2.8. Competitive Strategy

2.8.1. The Competitive Advantage
2.8.2. Choosing a Competitive Strategy
2.8.3. Strategies Based on the Strategic Clock Model
2.8.4. Types of Strategies According to the Industrial Sector Life Cycle

2.9.Strategic Management

2.9.1.The Concept of Strategy
2.9.2.The Process of Strategic Management
2.9.3.Approaches in Strategic Management

2.10. Strategy Implementation

2.10.1. Indicator Systems and Process Approach
2.10.2. Strategic Map
2.10.3. Strategic Alignment

2.11. Executive Management

2.11.1. Conceptual Framework of Executive Management
2.11.2. Executive Management The Role of the Board of Directors and Corporate Management Tools

2.12. Strategic Communication

2.12.1. Interpersonal Communication
2.12.2. Communication Skills and Influence
2.12.3. Internal Communication
2.12.4. Barriers to Business Communication

Module 3. People and Talent Management 

3.1. Organizational Behavior

3.1.1. Organizational Behavior Conceptual Framework
3.1.2. Main Factors of Organizational Behavior

3.2. People in Organizations

3.2.1. Quality of Work Life and Psychological Well-Being
3.2.2. Work Teams and Meeting Management
3.2.3. Coaching and Team Management
3.2.4. Managing Equality and Diversity

3.3. Strategic People Management

3.3.1. Strategic Human Resources Management
3.3.2. Strategic People Management

3.4. Evolution of Resources.An Integrated Vision

3.4.1. The Importance of HR
3.4.2. A New Environment for People Management and Leadership
3.4.3. Strategic HR Management

3.5.Selection, Group Dynamics and HR Recruitment

3.5.1. Approach to Recruitment and Selection
3.5.2. Recruitment
3.5.3. The Selection Process

3.6. Human Resources Management by Competencies

3.6.1. Analysis of the Potential
3.6.2. Remuneration Policy
3.6.3. Career/Succession Planning

3.7. Performance Evaluation and Compliance Management

3.7.1. Performance Management
3.7.2. Performance Management: Objectives and Process

3.8. Training Management

3.8.1. Learning Theories
3.8.2. Talent Detection and Retention
3.8.3. Gamification and Talent Management
3.8.4. Training and Professional Obsolescence

3.9. Talent Management

3.9.1. Keys for Positive Management
3.9.2. Conceptual Origin of Talent and its Implication in the Company
3.9.3. Map of Talent in the Organization
3.9.4. Cost and Added Value

3.10. Innovation in Talent and People Management

3.10.1. Strategic Talent Management Models
3.10.2. Identification, Training and Development of Talent
3.10.3. Loyalty and Retention
3.10.4. Proactivity and Innovation

3.11. Motivation

3.11.1. The Nature of Motivation
3.11.2. Expectations Theory
3.11.3. Needs Theory
3.11.4. Motivation and Financial Compensation

3.12. Employer Branding

3.12.1. Employer Branding in HR
3.12.2. Personal Branding for HR Professionals

3.13. Developing High Performance Teams

3.13.1. High-Performance Teams: Self-Managed Teams
3.13.2. Methodologies for the Management of High Performance Self-Managed Teams

3.14. Management Skills Development

3.14.1. What are Manager Competencies?
3.14.2. Elements of Competencies
3.14.3. Knowledge
3.14.4. Management Skills
3.14.5. Attitudes and Values in Managers
3.14.6. Managerial Skills

3.15. Time Management

3.15.1. Benefits
3.15.2. What Can be the Causes of Poor Time Management?
3.15.3. Time
3.15.4. Time Illusions
3.15.5. Attention and Memory
3.15.6.  State of Mind
3.15.7. Time Management
3.15.8. Being Proactive
3.15.9. Be Clear About the Objective
3.15.10. Order
3.15.11. Planning

3.16. Change Management

3.16.1. Change Management
3.16.2. Type of Change Management Processes
3.16.3. Stages or Phases in the Change Management Process

3.17. Negotiation and Conflict Management

3.17.1 Negotiation
3.17.2 Conflict Management
3.17.3 Crisis Management

3.18. Executive Communication

3.18.1. Internal and External Communication in the Corporate Environment
3.18.2. Communication Departments
3.18.3. The Person in Charge of Communication of the Company. The Profile of the Dircom

3.19. Human Resources Management and PRL Teams

3.19.1. Management of Human Resources and Teams
3.19.2. Prevention of Occupational Hazards

3.20. Productivity, Attraction, Retention and Activation of Talent

3.20.1. Productivity
3.20.2. Talent Attraction and Retention Levers

3.21. Monetary Compensation Vs. Non-Cash

3.21.1. Monetary Compensation Vs. Non-Cash
3.21.2. Wage Band Models
3.21.3. Non-cash Compensation Models
3.21.4. Working Model
3.21.5. Corporate Community
3.21.6. Company Image
3.21.7. Emotional Salary

3.22. Innovation in Talent and People Management II

3.22.1. Innovation in Organizations
3.22.2. New Challenges in the Human Resources Department
3.22.3. Innovation Management
3.22.4. Tools for Innovation

3.23. Knowledge and Talent Management

3.23.1. Knowledge and Talent Management
3.23.2. Knowledge Management Implementation

3.24. Transforming Human Resources in the Digital Era

3.24.1. The Socioeconomic Context
3.24.2. New Forms of Corporate Organization
3.24.3. New Methodologies

Module 4. Economic and Financial Management 

4.1. Economic Environment

4.1.1. Macroeconomic Environment and the National Financial System
4.1.2. Financial Institutions
4.1.3. Financial Markets
4.1.4. Financial Assets
4.1.5. Other Financial Sector Entities

4.2. Company Financing

4.2.1. Sources of Financing
4.2.2. Types of Financing Costs

4.3. Executive Accounting

4.3.1. Basic Concepts
4.3.2. The Company's Assets
4.3.3. The Company's Liabilities
4.3.4. The Company's Net Worth
4.3.5. The Income Statement

4.4. From General Accounting to Cost Accounting

4.4.1. Elements of Cost Calculation
4.4.2. Expenses in General Accounting and Cost Accounting
4.4.3. Costs Classification

4.5. Information Systems and Business Intelligence

4.5.1. Fundamentals and Classification
4.5.2. Cost Allocation Phases and Methods
4.5.3. Choice of Cost Center and Impact

4.6. Budget and Management Control

4.6.1. The Budget Model
4.6.2. The Capital Budget
4.6.3. The Operating Budget
4.6.5. Treasury Budget
4.6.6. Budget Monitoring

4.7. Treasury Management

4.7.1 .Accounting Working Capital and Necessary Working Capital
4.7.2. Calculation of Operating Requirements of Funds
4.7.3. Credit Management

4.8. Corporate Tax Responsibility

4.8.1. Basic Tax Concepts
4.8.2. Corporate Income Tax
4.8.3. Value Added Tax
4.8.4. Other Taxes Related to CommercialActivity
4.8.5. The Company as a Facilitator of the Work of the of the State

4.9. Systems of Control of Enterprises

4.9.1. Analysis of Financial Statements
4.9.2. The Company's Balance Sheet
4.9.3. The Profit and Loss Statement
4.9.4. The Statement of Cash Flows
4.9.5. Ratio Analysis

4.10. Financial Management

4.10.1. The Company's Financial Decisions
4.10.2. Financial Department
4.10.3. Cash Surpluses
4.10.4. Risks Associated with Financial Management
4.10.5. Financial Administration Risk Management

4.11. Financial Planning

4.11.1. Definition of Financial Planning
4.11.2. Actions to be Taken in Financial Planning
4.11.3. Creation and Establishment of the Business Strategy
4.11.4. The Cash Flow Table
4.11.5. The Working Capital Table

4.12. Corporate Financial Strategy

4.12.1. Corporate Strategy and Sources of Financing
4.12.2. Financial Products for Corporate Financing

4.13. Macroeconomic Context

4.13.1. Macroeconomic Context
4.13.2. Relevant Economic Indicators
4.13.3. Mechanisms for Monitoring of Macroeconomic Magnitudes
4.13.4. Economic Cycles

4.14. Strategic Financing

4.14.1. Self-Financing
4.14.2. Increase in Equity
4.14.3. Hybrid Resources
4.14.4. Financing Through Intermediaries

4.15. Money and Capital Markets

4.15.1. The Money Market
4.15.2. The Fixed Income Market
4.15.3. The Equity Market
4.15.4. The Foreign Exchange Market
4.15.5. The Derivatives Market

4.16. Financial Analysis and Planning

4.16.1. Analysis of the Balance Sheet
4.16.2. Analysis of the Income Statement
4.16.3. Profitability Analysis

4.17. Analysis and Resolution of Cases/Problems

4.17.1. Financial Information on Industria de Diseño y Textil, S.A. (INDITEX)

Module 5. Operations and Logistics Management 

5.1. Operations Direction and Management

5.1.1. The Role of Operations 
5.1.2. The Impact of Operations on the Management of Companies 
5.1.3. Introduction to Operations Strategy 
5.1.4. Operations Management

5.2. Industrial Organization and Logistics 

5.2.1. Industrial Organization Department
5.2.2. Logistics Department

5.3. Structure and Types of Production (MTS, MTO, ATO, ETO, etc) 

5.3.1. Production System 
5.3.2. Production Strategy 
5.3.3. Inventory Management System
5.3.4. Production Indicators 

5.4. Structure and Types of Procurement 

5.4.1. Function of Procurement 
5.4.2. Procurement Management
5.4.3. Types of Purchases 
5.4.4. Efficient Purchasing Management of a Company 
5.4.5. Stages of the Purchase Decision Process 

5.5. Economic Control of Purchasing 

5.5.1. Economic Influence of Purchases
5.5.2. Cost Centers 
5.5.3. Budget
5.5.4. Budgeting vs. Actual Expenditure
5.5.5. Budgetary Control Tools

5.6. Warehouse Operations Control 

5.6.1. Inventory Control
5.6.2. Location Systems
5.6.3. Stock Management Techniques
5.6.4. Storage Systems

5.7. Strategic Purchasing Management

5.7.1. Business Strategy
5.7.2. Strategic Planning
5.7.3. Purchasing Strategies

5.8. Typologies of the Supply Chain (SCM) 

5.8.1. Supply Chain
5.8.2. Benefits of Supply Chain Management
5.8.3. Logistical Management in the Supply Chain

5.9. Supply Chain Management 

5.9.1. The Concept of Management of the Supply Chain (SCM)
5.9.2. Supply Chain Costs and Efficiency
5.9.3. Demand Patterns
5.9.4. Operations Strategy and Change

5.10. Interactions Between the SCM and All Other Departments

5.10.1. Interaction of the Supply Chain 
5.10.2. Interaction of the Supply Chain. Integration by Parts
5.10.3. Supply Chain Integration Problems 
5.10.4. Supply Chain

5.11. Logistics Costs 

5.11.1. Logistics Costs
5.11.2. Problems with Logistics Costs
5.11.3. Optimizing Logistic Costs 

5.12. Profitability and Efficiency of Logistics Chains: KPIS 

5.12.1. Logistics Chain
5.12.2. Profitability and Efficiency of the Logistics Chain
5.12.3. Indicators of Profitability and Efficiency of the Logistics Chain

5.13. Process Management

5.13.1. Process Management 
5.13.2. Process-Based Approach: Process Mapping 
5.13.3. Improvements in Process Management 

5.14. Distribution and Transportation and Logistics

5.14.1. Distribution in the Supply Chain 
5.14.2. Transportation Logistics 
5.14.3. Geographic Information Systems as a Support to Logistics 

5.15. Logistics and Customers

5.15.1. Demand Analysis 
5.15.2. Demand and Sales Forecast 
5.15.3. Sales and Operations Planning
5.15.4. Participatory Planning, Forecasting and Replenishment Planning (CPFR) 

5.16. International Logistics 

5.16.1. Export and Import Processes 
5.16.2. Customs 
5.16.3. Methods and Means of International Payment
5.16.4. International Logistics Platforms

5.17. Outsourcing of Operations

5.17.1. Operations Management and Outsourcing 
5.17.2. Outsourcing Implementation in Logistics Environments 

5.18. Competitiveness in Operations

5.18.1. Operations Management 
5.18.2. Operational Competitiveness 
5.18.3. Operations Strategy and Competitive Advantages 

5.19. Quality Management

5.19.1. Internal and External Customers
5.19.2. Quality Costs
5.19.3. Ongoing Improvement and the Deming Philosophy

Module 6. Information Systems Management

6.1. Technological Environment

6.1.1. Technology and Globalization 
6.1.2. Economic Environment and Technology 
6.1.3. Technological Environment and Its Impact on Companies 

6.2. Information Systems and Technologies in the Enterprise 

6.2.1. The Evolution of the IT Model
6.2.2. Organization and IT Departments
6.2.3. Information Technology and Economic Environment

6.3. Corporate Strategy and Technology Strategy 

6.3.1. Creating Value for Customers and Shareholders
6.3.2. Strategic IS/IT Decisions
6.3.3. Corporate Strategy Vs. Technology and Digital Strategy

6.4. Information Systems Management

6.4.1. Corporate Governance of Technology and Information Systems 
6.4.2. Management of Information Systems in Companies 
6.4.3. Expert Managers in Information Systems: Roles and Functions 

6.5. Information Technology Strategic Planning

6.5.1. Information Systems and Corporate Strategy
6.5.2. Strategic Planning of Information Systems 
6.5.3. Phases of Information Systems Strategic Planning 

6.6. Information Systems for Decision-Making

6.6.1. Business Intelligence
6.6.2. Data Warehouse
6.6.3. BSC or Balanced Scorecard

6.7. Exploring the Information

6.7.1. SQL: Relational Databases.Basic Concepts
6.7.2. Networks and Communications
6.7.3. Operational System: Standardized Data Models 
6.7.4. Strategic System: OLAP, Multidimensional Model and Graphical Dashboards 
6.7.5. Strategic DB Analysis and Report Composition 

6.8. Enterprise Business Intelligence

6.8.1. The World of Data
6.8.2. Relevant Concepts
6.8.3. Main Characteristics
6.8.4. Solutions in Today's Market
6.8.5. Overall Architecture of a BI Solution
6.8.6. Cybersecurity in BI and Data Science

6.9. New Business Concept 

6.9.1. Why BI
6.9.2. Obtaining Information
6.9.3. BI in the Different Departments of the Company
6.9.4. Reasons to Invest in BI

6.10. BI Tools and Solutions 

6.10.1. How to Choose the Best Tool?
6.10.2. Microsoft Power BI, MicroStrategy and Tableau
6.10.3. SAP BI, SAS BI and Qlikview
6.10.4. Prometheus

6.11. BI Project Planning and Management 

6.11.1. First Steps to Define a BI Project
6.11.2. BI Solution for the Company
6.11.3. Requirements and Objectives 

6.12. Corporate Management Applications 

6.12.1. Information Systems and Corporate Management 
6.12.2. Applications for Corporate Management 
6.12.3. Enterprise Resource Planning or ERP Systems 

6.13. Digital Transformation

6.13.1. Conceptual Framework of Digital Transformation 
6.13.2. Digital Transformation; Key Elements, Benefits and Drawbacks 
6.13.3. Digital Transformation in Companies 

6.14. Technology and Trends

6.14.1. Main Trends in the Field of Technology that are Changing Business Models 
6.14.2. Analysis of the Main Emerging Technologies 

6.15. IT Outsourcing

6.15.1. Conceptual Framework of Outsourcing 
6.15.2. IT Outsourcing and its Impact on the Business 
6.15.3. Keys to Implement Corporate IT Outsourcing Projects

Module 7. Commercial Management, Strategic Marketing and Corporate Communication

7.1. Commercial Management

7.1.1. Conceptual Framework of Commercial Management
7.1.2. Business Strategy and Planning
7.1.3. The Role of Sales Managers

7.2. Marketing 

7.2.1. The Concept of Marketing
7.2.2. Basic Elements of Marketing
7.2.3. Marketing Activities of the Company

7.3. Strategic Marketing Management

7.3.1. The Concept of Strategic Marketing
7.3.2. Concept of Strategic Marketing Planning
7.3.3. Stages in the Process of Strategic Marketing Planning

7.4. Digital Marketing and E-Commerce

7.4.1. Digital Marketing and E-Commerce Objectives 
7.4.2. Digital Marketing and Media Used 
7.4.3. E-Commerce General Context 
7.4.4. Categories of E-Commerce 
7.4.5. Advantages and Disadvantages of E-Commerce Versus Traditional Commerce 

7.5. Managing Digital Business

7.5.1. Competitive Strategy in the Face of the Growing Digitalization of the Media 
7.5.2. Design and Creation of a Digital Marketing Plan 
7.5.3. ROI Analysis in a Digital Marketing Plan 

7.6. Digital Marketing to Reinforce the Brand

7.6.1. Online Strategies to Improve Your Brand's Reputation
7.6.2. Branded Content and Storytelling

7.7. Digital Marketing Strategy

7.7.1. Defining the Digital Marketing Strategy 
7.7.2. Digital Marketing Strategy Tools 

7.8. Digital Marketing to Attract and Retain Customers 

7.8.1. Loyalty and Engagement Strategies Through the Internet
7.8.2. Visitor Relationship Management
7.8.3. Hypersegmentation

7.9. Managing Digital Campaigns

7.9.1. What is a Digital Advertising Campaign?
7.9.2. Steps to Launch an Online Marketing Campaign
7.9.3. Mistakes in Digital Advertising Campaigns

7.10. Online Marketing Plan

7.10.1. What is an Online Marketing Plan?
7.10.2. Steps to Create an Online Marketing Plan
7.10.3. Advantages of Having an Online Marketing Plan

7.11. Blended Marketing

7.11.1. What is Blended Marketing?
7.11.2. Differences Between Online and Offline Marketing
7.11.3. Aspects to be Taken into Account in the Blended Marketing Strategy 
7.11.4. Characteristics of a Blended Marketing Strategy
7.11.5. Recommendations in Blended Marketing
7.11.6. Benefits of Blended Marketing

7.12. Sales Strategy 

7.12.1. Sales Strategy 
7.12.2. Sales Methods

7.13. Corporate Communication 

7.13.1. Concept
7.13.2. The Importance of Communication in the Organization
7.13.3. Type of Communication in the Organization
7.13.4. Functions of Communication in the Organization
7.13.5. Elements of Communication
7.13.6. Communication Problems
7.13.7. Communication Scenarios

7.14. Corporate Communication Strategy 

7.14.1. Motivational Programs, Social Action, Participation and Training with HR 
7.14.2. Internal Communication Tools and Supports
7.14.3. Internal Communication Plan

7.15. Digital Communication and Reputation

7.15.1. Online Reputation 
7.15.2. How to Measure Digital Reputation? 
7.15.3. Online Reputation Tools 
7.15.4. Online Reputation Report 
7.15.5. Online Branding

Module 8. Market Research, Advertising and Commercial Management

8.1. Market Research 

8.1.1. Marketing Research: Historical Origin 
8.1.2. Analysis and Evolution of the Conceptual Framework of Marketing Research 
8.1.3. Key Elements and Value Contribution of Market Research 

8.2. Quantitative Research Methods and Techniques 

8.2.1. Sample Size 
8.2.2. Sampling 
8.2.3. Types of Quantitative Techniques 

8.3. Qualitative Research Methods and Techniques 

8.3.1. Types of Qualitative Research 
8.3.2. Qualitative Research Techniques 

8.4. Market Segmentation 

8.4.1. Market Segmentation Concept 
8.4.2. Utility and Segmentation Requirements 
8.4.3. Consumer Market Segmentation 
8.4.4. Industrial Market Segmentation 
8.4.5. Segmentation Strategies 
8.4.6. Segmentation Based on Marketing - Mix Criteria 
8.4.7. Market Segmentation Methodology 

8.5. Research Project Management 

8.5.1. Market Research as a Process 
8.5.2. Planning Stages in Market Research 
8.5.3. Stages of Market Research Implementation 
8.5.4. Managing a Research Project 

8.6. International Market Research 

8.6.1. International Market Research 
8.6.2. International Market Research Process 
8.6.3. The Importance of Secondary Sources in International Market Research 

8.7. Feasibility Studies 

8.7.1. Concept and Usefulness 
8.7.2. Outline of a Feasibility Study 
8.7.3. Development of a Feasibility Study 

8.8. Publicity 

8.8.1. Historical Background of Advertising 
8.8.2. Conceptual Framework of Advertising; Principles, Concept of Briefing and Positioning 
8.8.3. Advertising Agencies, Media Agencies and Advertising Professionals 
8.8.4. Importance of Advertising in Business 
8.8.5. Advertising Trends and Challenges 

8.9. Developing the Marketing Plan 

8.9.1. Marketing Plan Concept 
8.9.2. Situation Analysis and Diagnosis 
8.9.3. Strategic Marketing Decisions 
8.9.4. Operational Marketing Decisions 

8.10. Strategies 

8.10.1. Integrated Marketing Communication 
8.10.2. Advertising Communication Plan 
8.10.3. Merchandising as a Communication Technique 

8.11. Media Planning 

8.11.1. Origin and Evolution of Media Planning 
8.11.2. Media 
8.11.3. Media Plan 

8.12. Fundamentals of Commercial Management 

8.12.1. The Role of Commercial Management 
8.12.2. Systems of Analysis of the Company/Market Commercial Competitive Situation 
8.12.3. Commercial Planning Systems of the Company 
8.12.4. Main Competitive Strategies

8.13. Commercial Negotiation 

8.13.1. Commercial Negotiation 
8.13.2. Psychological Issues in Negotiation 
8.13.3. Main Negotiation Methods 
8.13.4. The Negotiation Process 

8.14. Decision-Making in Commercial Management 

8.14.1. Commercial Strategy and Competitive Strategy 
8.14.2. Decision Making Models 
8.14.3. Decision-Making Analytics and Tools 
8.14.4. Human Behavior in Decision Making 

8.15. Leadership and Management of the Sales Network 

8.15.1. Sales Management Sales Management 
8.15.2. Networks Serving Commercial Activity 
8.15.3. Salesperson Recruitment and Training Policies 
8.15.4. Remuneration Systems for Own and External Commercial Networks 
8.15.5. Management of the Commercial Process Control and Assistance to the Work of the Sales Representatives Based on the Information 

8.16. Implementing the Commercial Function 

8.16.1. Recruitment of Own Sales Representatives and Sales Agents 
8.16.2. Controlling Commercial Activity 
8.16.3. The Code of Ethics of Sales Personnel 
8.16.4. Compliance with Legislation 
8.16.5. Generally Accepted Standards of Business Conduct 

8.17. Key Account Management 

8.17.1. Concept of Key Account Management 
8.17.2. The Key Account Manager 
8.17.3. Key Account Management Strategy 

8.18. Financial and Budgetary Management 

8.18.1. The Break-Even Point 
8.18.2. The Sales Budget Control of Management and of the Annual Sales Plan 
8.18.3. Financial Impact of Strategic Sales Decisions 
8.18.4. Cycle Management, Turnover, Profitability and Liquidity
8.18.5. Income Statement

Module 9. Innovation and Project Management

9.1. Innovation

9.1.1. Introduction to Innovation 
9.1.2. Innovation in the Entrepreneurial Ecosystem 
9.1.3. Instruments and Tools for the Business Innovation Process 

9.2. Innovation Strategy

9.2.1. Strategic Intelligence and Innovation 
9.2.2. Innovation from Strategy 

9.3. Project Management for Startups

9.3.1. Startup Concept
9.3.2. Lean Startup Philosophy
9.3.3. Stages of Startup Development
9.3.4. The Role of a Project Manager in a Startup

9.4. Business Model Design and Validation

9.4.1. Conceptual Framework of a Business Model 
9.4.2. Business Model Design and Validation 

9.5. Project Management

9.5.1. Project Management: Identification of Opportunities to Develop Corporate Innovation Projects 
9.5.2. Main Stages or Phases in the Direction and Management of Innovation Projects 

9.6. Project Change Management: Training Management 

9.6.1. Concept of Change Management
9.6.2. The Change Management Process
9.6.3. Change Implementation

9.7. Project Communication Management

9.7.1. Project Communications Management
9.7.2. Key Concepts for Project Communications Management
9.7.3. Emerging Trends
9.7.4. Adaptations to Equipment
9.7.5. Planning Communications Management
9.7.6. Manage Communications
9.7.7. Monitoring Communications

9.8. Traditional and Innovative Methodologies

9.8.1. Innovative Methodologies
9.8.2. Basic Principles of Scrum
9.8.3. Differences between the Main Aspects of Scrum and Traditional Methodologies

9.9. Creation of a Startup

9.9.1. Creation of a Startup 
9.9.2. Organization and Culture
9.9.3. Top Ten Reasons Why Startups Fail 
9.9.4. Legal Aspects

9.10. Project Risk Management Planning

9.10.1. Risk Planning
9.10.2. Elements for Creating a Risk Management Plan
9.10.3. Tools for Creating a Risk Management Plan
9.10.4. Content of the Risk Management Plan

Module 10. Executive Management

10.1. General Management

10.1.1. The Concept of General Management 
10.1.2. The General Manager's Action 
10.1.3. The CEO and Their Responsibilities 
10.1.4. Transforming the Work of Management 

10.2. Manager Functions:Organizational Culture and Approaches 

10.2.1. Manager Functions: Organizational Culture and Approaches 

10.3. Operations Management 

10.3.1. The Importance of Management 
10.3.2. Value Chain 
10.3.3. Quality Management 

10.4. Public Speaking and Spokesperson Education 

10.4.1. Interpersonal Communication 
10.4.2. Communication Skills and Influence 
10.4.3. Communication Barriers 

10.5. Personal and Organizational Communications Tools 

10.5.1. Interpersonal Communication 
10.5.2. Interpersonal Communication Tools 
10.5.3. Communication in the Organization 
10.5.4. Tools in the Organization 

10.6. Communication in Crisis Situations 

10.6.1. Crisis 
10.6.2. Phases of the Crisis 
10.6.3. Messages: Contents and Moments 

10.7. Preparation of a Crisis Plan 

10.7.1. Analysis of Possible Problems 
10.7.2. Planning 
10.7.3. Adequacy of Personnel 

10.8. Emotional Intelligence 

10.8.1. Emotional Intelligence and Communication 
10.8.2. Assertiveness, Empathy, and Active Listening 
10.8.3. Self-Esteem and Emotional Communication 

10.9. Personal Branding 

10.9.1. Strategies to Develop Personal Branding 
10.9.2. Personal Branding Laws 
10.9.3. Tools for Creating Personal Brands 

10.10. Leadership and Team Management 

10.10.1. Leadership and Leadership Styles 
10.10.2. Leader Capabilities and Challenges 
10.10.3. Managing Change Processes 
10.10.4. Managing Multicultural Teams

Module 11. Fundamentals of Artificial Intelligence

11.1. History of Artificial Intelligence 

11.1.1. When Do We Start Talking About Artificial Intelligence? 
11.1.2. References in Film 
11.1.3. Importance of Artificial Intelligence 
11.1.4. Technologies that Enable and Support Artificial Intelligence 

11.2. Artificial Intelligence in Games 

11.2.1. Game Theory 
11.2.2. Minimax and Alpha-Beta Pruning 
11.2.3. Simulation: Monte Carlo 

11.3. Neural Networks 

11.3.1. Biological Fundamentals 
11.3.2. Computational Model 
11.3.3. Supervised and Unsupervised Neural Networks 
11.3.4. Simple Perceptron 
11.3.5. Multilayer Perceptron 

11.4. Genetic Algorithms 

11.4.1. History 
11.4.2. Biological Basis 
11.4.3. Problem Coding 
11.4.4. Generation of the Initial Population 
11.4.5. Main Algorithm and Genetic Operators 
11.4.6. Evaluation of Individuals: Fitness 

11.5. Thesauri, Vocabularies, Taxonomies 

11.5.1. Vocabulary 
11.5.2. Taxonomy 
11.5.3. Thesauri 
11.5.4. Ontologies 
11.5.5. Knowledge Representation: Semantic Web 

11.6. Semantic Web 

11.6.1. Specifications: RDF, RDFS and OWL 
11.6.2. Inference/ Reasoning 
11.6.3. Linked Data 

11.7. Expert Systems and DSS 

11.7.1. Expert Systems 
11.7.2. Decision Support Systems 

11.8. Chatbots and Virtual Assistants 

11.8.1. Types of Assistants: Voice and Text Assistants 
11.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialogue Flow 
11.8.3. Integrations: Web, Slack, WhatsApp, Facebook 
11.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant 

11.9. AI Implementation Strategy 
11.10. Future of Artificial Intelligence 

11.10.1. Understand How to Detect Emotions Using Algorithms 
11.10.2. Creating a Personality:Language, Expressions and Content 
11.10.3. Trends of Artificial Intelligence 
11.10.4. Reflections 

Module 12. Data Types and Data Life Cycle

12.1. Statistics 

12.1.1. Statistics: Descriptive Statistics, Statistical Inferences 
12.1.2. Population, Sample, Individual 
12.1.3. Variables: Definition, Measurement Scales 

12.2. Types of Data Statistics 

12.2.1. According to Type 

12.2.1.1. Quantitative: Continuous Data and Discrete Data 
12.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data 

12.2.2. According to their Shape 

12.2.2.1. Numeric 
12.2.2.2. Text: 
12.2.2.3. Logical 

12.2.3. According to its Source 

12.2.3.1. Primary 
12.2.3.2. Secondary 

12.3. Life Cycle of Data 

12.3.1. Stages of the Cycle 
12.3.2. Milestones of the Cycle 
12.3.3. FAIR Principles 

12.4. Initial Stages of the Cycle 

12.4.1. Definition of Goals 
12.4.2. Determination of Resource Requirements 
12.4.3. Gantt Chart 
12.4.4. Data Structure 

12.5. Data Collection 

12.5.1. Methodology of Data Collection 
12.5.2. Data Collection Tools 
12.5.3. Data Collection Channels 

12.6. Data Cleaning 

12.6.1. Phases of Data Cleansing 
12.6.2. Data Quality 
12.6.3. Data Manipulation (with R) 

12.7. Data Analysis, Interpretation and Result Evaluation 

12.7.1. Statistical Measures 
12.7.2. Relationship Indexes 
12.7.3. Data Mining 

12.8. Datawarehouse 

12.8.1. Elements that Comprise it 
12.8.2. Design 
12.8.3. Aspects to Consider 

12.9. Data Availability 

12.9.1. Access 
12.9.2. Uses 
12.9.3. Security 

12.10. Regulatory Framework 

12.10.1. Data Protection Law 
12.10.2. Good Practices 
12.10.3. Other Regulatory Aspects 

Module 13. Data in Artificial Intelligence

13.1. Data Science 

13.1.1. Data Science 
13.1.2. Advanced Tools for the Data Scientist 

13.2. Data, Information and Knowledge 

13.2.1. Data, Information and Knowledge 
13.2.2. Types of Data 
13.2.3. Data Sources 

13.3. From Data to Information 

13.3.1. Data Analysis 
13.3.2. Types of Analysis 
13.3.3. Extraction of Information from a Dataset 

13.4. Extraction of Information Through Visualization 

13.4.1. Visualization as an Analysis Tool 
13.4.2. Visualization Methods 
13.4.3. Visualization of a Data Set 

13.5. Data Quality 

13.5.1. Quality Data 
13.5.2. Data Cleaning 
13.5.3. Basic Data Pre-Processing 

13.6. Dataset 

13.6.1. Dataset Enrichment 
13.6.2. The Curse of Dimensionality 
13.6.3. Modification of Our Data Set 

13.7. Unbalance 

13.7.1. Classes of Unbalance 
13.7.2. Unbalance Mitigation Techniques 
13.7.3. Balancing a Dataset 

13.8. Unsupervised Models 

13.8.1. Unsupervised Model 
13.8.2. Methods 
13.8.3. Classification with Unsupervised Models 

13.9. Supervised Models 

13.9.1. Supervised Model 
13.9.2. Methods 
13.9.3. Classification with Supervised Models 

13.10. Tools and Good Practices 

13.10.1. Good Practices for Data Scientists 
13.10.2. The Best Model 
13.10.3. Useful Tools 

Module 14. Data Mining Selection, Pre-Processing and Transformation

14.1. Statistical Inference 

14.1.1. Descriptive Statistics vs. Statistical Inference 
14.1.2. Parametric Procedures 
14.1.3. Non-Parametric Procedures 

14.2. Exploratory Analysis 

14.2.1. Descriptive Analysis 
14.2.2. Visualization 
14.2.3. Data Preparation 

14.3. Data Preparation 

14.3.1. Integration and Data Cleaning 
14.3.2. Normalization of Data 
14.3.3. Transforming Attributes 

14.4. Missing Values 

14.4.1. Treatment of Missing Values 
14.4.2. Maximum Likelihood Imputation Methods 
14.4.3. Missing Value Imputation Using Machine Learning 

14.5. Noise in the Data 

14.5.1. Noise Classes and Attributes 
14.5.2. Noise Filtering 
14.5.3. The Effect of Noise 

14.6. The Curse of Dimensionality 

14.6.1. Oversampling 
14.6.2. Undersampling 
14.6.3. Multidimensional Data Reduction 

14.7. From Continuous to Discrete Attributes 

14.7.1. Continuous Data Vs. Discreet Data 
14.7.2. Discretization Process 

14.8. The Data 

14.8.1. Data Selection 
14.8.2. Prospects and Selection Criteria 
14.8.3. Selection Methods 

14.9. Instance Selection 

14.9.1. Methods for Instance Selection 
14.9.2. Prototype Selection 
14.9.3. Advanced Methods for Instance Selection 

14.10. Data Pre-Processing in Environments 

Module 15. Algorithm and Complexity in Artificial Intelligence

15.1. Introduction to Algorithm Design Strategies 

15.1.1. Recursion 
15.1.2. Divide and Conquer 
15.1.3. Other Strategies 

15.2. Efficiency and Analysis of Algorithms 

15.2.1. Efficiency Measures 
15.2.2. Measuring the Size of the Input 
15.2.3. Measuring Execution Time 
15.2.4. Worst, Best and Average Case 
15.2.5. Asymptotic Notation 
15.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms 
15.2.7. Mathematical Analysis of Recursive Algorithms 
15.2.8. Empirical Analysis of Algorithms 

15.3. Sorting Algorithms 

15.3.1. Concept of Sorting 
15.3.2. Bubble Sorting 
15.3.3. Sorting by Selection 
15.3.4. Sorting by Insertion 
15.3.5. Merge Sort 
15.3.6. Quick Sort 

15.4. Algorithms with Trees 

15.4.1. Tree Concept 
15.4.2. Binary Trees 
15.4.3. Tree Paths 
15.4.4. Representing Expressions 
15.4.5. Ordered Binary Trees 
15.4.6. Balanced Binary Trees 

15.5. Algorithms Using Heaps 

15.5.1. Heaps 
15.5.2. The Heapsort Algorithm 
15.5.3. Priority Queues 

15.6. Graph Algorithms 

15.6.1. Representation 
15.6.2. Traversal in Width 
15.6.3. Depth Travel 
15.6.4. Topological Sorting 

15.7. Greedy Algorithms 

15.7.1. Greedy Strategy 
15.7.2. Elements of the Greedy Strategy 
15.7.3. Currency Exchange 
15.7.4. Traveler’s Problem 
15.7.5. Backpack Problem 

15.8. Minimal Path Finding 

15.8.1. The Minimum Path Problem 
15.8.2. Negative Arcs and Cycles 
15.8.3. Dijkstra's Algorithm 

15.9.  Algorithms on Graphs 

15.9.1. The Minimum Covering Tree 
15.9.2. Prim's Algorithm 
15.9.3. Kruskal’s Algorithm 
15.9.4. Complexity Analysis 

15.10. Backtracking 

15.10.1. Backtracking 
15.10.2. Alternative Techniques 

Module 16. Intelligent Systems

16.1. Agent Theory 

16.1.1. Concept History 
16.1.2. Agent Definition 
16.1.3. Agents in Artificial Intelligence 
16.1.4. Agents in Software Engineering 

16.2. Agent Architectures 

16.2.1. The Reasoning Process of an Agent 
16.2.2. Reactive Agents 
16.2.3. Deductive Agents 
16.2.4. Hybrid Agents 
16.2.5. Comparison 

16.3. Information and Knowledge 

16.3.1. Difference between Data, Information and Knowledge 
16.3.2. Data Quality Assessment 
16.3.3. Data Collection Methods 
16.3.4. Information Acquisition Methods 
16.3.5. Knowledge Acquisition Methods 

16.4. Knowledge Representation 

16.4.1. The Importance of Knowledge Representation 
16.4.2. Definition of Knowledge Representation According to Roles 
16.4.3. Knowledge Representation Features 

16.5. Ontologies 

16.5.1. Introduction to Metadata 
16.5.2. Philosophical Concept of Ontology 
16.5.3. Computing Concept of Ontology 
16.5.4. Domain Ontologies and Higher-Level Ontologies 
16.5.5. How to Build an Ontology? 

16.6. Ontology Languages and Ontology Creation Software 

16.6.1. Triple RDF, Turtle and N 
16.6.2. RDF Schema 
16.6.3. OWL 
16.6.4. SPARQL 
16.6.5. Introduction to Ontology Creation Tools 
16.6.6. Installing and Using Protégé 

16.7. Semantic Web 

16.7.1. Current and Future Status of the Semantic Web 
16.7.2. Semantic Web Applications 

16.8. Other Knowledge Representation Models 

16.8.1. Vocabulary 
16.8.2. Global Vision 
16.8.3. Taxonomy 
16.8.4. Thesauri 
16.8.5. Folksonomy 
16.8.6. Comparison 
16.8.7. Mind Maps 

16.9. Knowledge Representation Assessment and Integration 

16.9.1. Zero-Order Logic 
16.9.2. First-Order Logic 
16.9.3. Descriptive Logic 
16.9.4. Relationship between Different Types of Logic 
16.9.5. Prolog: Programming Based on First-Order Logic 

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

16.10.1. Concept of Reasoner 
16.10.2. Reasoner Applications 
16.10.3. Knowledge-Based Systems 
16.10.4. MYCIN: History of Expert Systems 
16.10.5. Expert Systems Elements and Architecture 
16.10.6. Creating Expert Systems 

Module 17. Machine Learning and Data Mining

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

17.1.1. Key Concepts of Knowledge Discovery Processes 
17.1.2. Historical Perspective of Knowledge Discovery Processes 
17.1.3. Stages of the Knowledge Discovery Processes 
17.1.4. Techniques Used in Knowledge Discovery Processes 
17.1.5. Characteristics of Good Machine Learning Models 
17.1.6. Types of Machine Learning Information 
17.1.7. Basic Learning Concepts 
17.1.8. Basic Concepts of Unsupervised Learning 

17.2. Data Exploration and Pre-Processing 

17.2.1. Data Processing 
17.2.2. Data Processing in the Data Analysis Flow 
17.2.3. Types of Data 
17.2.4. Data Transformations 
17.2.5. Visualization and Exploration of Continuous Variables 
17.2.6. Visualization and Exploration of Categorical Variables 
17.2.7. Correlation Measures 
17.2.8. Most Common Graphic Representations 
17.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction 

17.3. Decision Trees 

17.3.1. ID Algorithm 
17.3.2. Algorithm C 
17.3.3. Overtraining and Pruning 
17.3.4. Result Analysis 

17.4. Evaluation of Classifiers 

17.4.1. Confusion Matrixes 
17.4.2. Numerical Evaluation Matrixes 
17.4.3. Kappa Statistic 
17.4.4. ROC Curves 

17.5. Classification Rules 

17.5.1. Rule Evaluation Measures 
17.5.2. Introduction to Graphic Representation 
17.5.3. Sequential Overlay Algorithm 

17.6. Neural Networks 

17.6.1. Basic Concepts 
17.6.2. Simple Neural Networks 
17.6.3. Backpropagation Algorithm 
17.6.4. Introduction to Recurrent Neural Networks 

17.7. Bayesian Methods 

17.7.1. Basic Probability Concepts 
17.7.2. Bayes' Theorem 
17.7.3. Naive Bayes 
17.7.4. Introduction to Bayesian Networks 

17.8. Regression and Continuous Response Models 

17.8.1. Simple Linear Regression 
17.8.2. Multiple Linear Regression 
17.8.3. Logistic Regression 
17.8.4. Regression Trees 
17.8.5. Introduction to Support Vector Machines (SVM) 
17.8.6. Goodness-of-Fit Measures 

17.9. Clustering 

17.9.1. Basic Concepts 
17.9.2. Hierarchical Clustering 
17.9.3. Probabilistic Methods 
17.9.4. EM Algorithm 
17.9.5. B-Cubed Method 
17.9.6. Implicit Methods 

17.10. Text Mining and Natural Language Processing (NLP) 

17.10.1. Basic Concepts 
17.10.2. Corpus Creation 
17.10.3. Descriptive Analysis 
17.10.4. Introduction to Feelings Analysis 

Module 18. Neural networks, the basis of Deep Learning

18.1. Deep Learning 

18.1.1. Types of Deep Learning 
18.1.2. Applications of Deep Learning 
18.1.3. Advantages and Disadvantages of Deep Learning 

18.2. Surgery 

18.2.1. Sum 
18.2.2. Product 
18.2.3. Transfer 

18.3. Layers 

18.3.1. Input Layer 
18.3.2. Cloak 
18.3.3. Output Layer 

18.4. Layer Bonding and Operations 

18.4.1. Architecture Design 
18.4.2. Connection between Layers 
18.4.3. Forward Propagation 

18.5. Construction of the First Neural Network 

18.5.1. Network Design 
18.5.2. Establish the Weights 
18.5.3. Network Training 

18.6. Trainer and Optimizer 

18.6.1. Optimizer Selection 
18.6.2. Establishment of a Loss Function 
18.6.3. Establishing a Metric 

18.7. Application of the Principles of Neural Networks 

18.7.1. Activation Functions 
18.7.2. Backward Propagation 
18.7.3. Parameter Adjustment 

18.8. From Biological to Artificial Neurons 

18.8.1. Functioning of a Biological Neuron 
18.8.2. Transfer of Knowledge to Artificial Neurons 
18.8.3. Establish Relations Between the Two 

18.9. Implementation of MLP (Multilayer Perceptron) with Keras 

18.9.1. Definition of the Network Structure 
18.9.2. Model Compilation 
18.9.3. Model Training 

18.10. Fine Tuning Hyperparameters of Neural Networks 

18.10.1. Selection of the Activation Function 
18.10.2. Set the Learning Rate 
18.10.3. Adjustment of Weights 

Module 19. Deep Neural Networks Training

19.1. Gradient Problems 

19.1.1. Gradient Optimization Techniques 
19.1.2. Stochastic Gradients 
19.1.3. Weight Initialization Techniques 

19.2. Reuse of Pre-Trained Layers 

19.2.1. Transfer Learning Training 
19.2.2. Feature Extraction 
19.2.3. Deep Learning 

19.3. Optimizers 

19.3.1. Stochastic Gradient Descent Optimizers 
19.3.2. Optimizers Adam and RMSprop 
19.3.3. Moment Optimizers 

19.4. Programming of the Learning Rate 

19.4.1. Automatic Learning Rate Control 
19.4.2. Learning Cycles 
19.4.3. Smoothing Terms 

19.5. Overfitting 

19.5.1. Cross Validation 
19.5.2. Regularization 
19.5.3. Evaluation Metrics 

19.6. Practical Guidelines 

19.6.1. Model Design 
19.6.2. Selection of Metrics and Evaluation Parameters 
19.6.3. Hypothesis Testing 

19.7. Transfer Learning 

19.7.1. Transfer Learning Training 
19.7.2. Feature Extraction 
19.7.3. Deep Learning 

19.8. Data Augmentation 

19.8.1. Image Transformations 
19.8.2. Synthetic Data Generation 
19.8.3. Text Transformation 

19.9. Practical Application of Transfer Learning 

19.9.1. Transfer Learning Training 
19.9.2. Feature Extraction 
19.9.3. Deep Learning 

19.10. Regularization 

19.10.1. L and L 
19.10.2. Regularization by Maximum Entropy 
19.10.3. Dropout 

Module 20. TensorFlow model customization and training

20.1. TensorFlow 

20.1.1. Use of the TensorFlow Library 
20.1.2. Model Training with TensorFlow 
20.1.3. Operations with Graphs in TensorFlow 

20.2. TensorFlow and NumPy 

20.2.1. NumPy Computing Environment for TensorFlow 
20.2.2. Using NumPy Arrays with TensorFlow 
20.2.3. NumPy Operations for TensorFlow Graphs 

20.3. Model Customization and Training Algorithms 

20.3.1. Building Custom Models with TensorFlow 
20.3.2. Management of Training Parameters 
20.3.3. Use of Optimization Techniques for Training 

20.4. TensorFlow Features and Graphs 

20.4.1. Functions with TensorFlow 
20.4.2. Use of Graphs for Model Training 
20.4.3. Grap Optimization with TensorFlow Operations 

20.5. Loading and Pre-Processing Data with TensorFlow 

20.5.1. Loading Data Sets with TensorFlow 
20.5.2. Pre-Processing Data with TensorFlow 
20.5.3. Using TensorFlow Tools for Data Manipulation 

20.6. The Tf.data API 

20.6.1. Using the Tf.data API for Data Processing 
20.6.2. Construction of Data Streams with Tf.data 
20.6.3. Using the Tf.data API for Model Training 

20.7. The TFRecord Format 

20.7.1. Using the TFRecord API for Data Serialization 
20.7.2. TFRecord File Upload with TensorFlow 
20.7.3. Using TFRecord Files for Model Training 

20.8. Keras Preprocessing Layers 

20.8.1. Using the Keras Preprocessing API 
20.8.2. Preprocessing Pipelined Construction with Keras 
20.8.3. Using the Keras Preprocessing API for Model Training 

20.9. The TensorFlow Datasets Project 

20.9.1. Using TensorFlow Datasets for Data Loading 
20.9.2. Pre-Processing Data with TensorFlow Datasets 
20.9.3. Using TensorFlow Datasets for Model Training 

20.10. Building a Deep Learning App with TensorFlow 

20.10.1. Practical Applications 
20.10.2. Building a Deep Learning App with TensorFlow 
20.10.3. Model Training with TensorFlow 
20.10.4. Use of the Application for the Prediction of Results 

Module 21. Deep Computer Vision with Convolutional Neural Networks 

21.1. The Visual Cortex Architecture 

21.1.1. Functions of the Visual Cortex 
21.1.2. Theories of Computational Vision 
21.1.3. Models of Image Processing 

21.2. Convolutional Layers 

21.2.1. Reuse of Weights in Convolution 
21.2.2. Convolution D 
21.2.3. Activation Functions 

21.3. Grouping Layers and Implementation of Grouping Layers with Keras 

21.3.1. Pooling and Striding 
21.3.2. Flattening 
21.3.3. Types of Pooling 

21.4. CNN Architecture 

21.4.1. VGG Architecture 
21.4.2. AlexNet Architecture 
21.4.3. ResNet Architecture 

21.5. Implementing a CNN ResNet using Keras 

21.5.1. Weight Initialization 
21.5.2. Input Layer Definition 
21.5.3. Output Definition 

21.6. Use of Pre-Trained Keras Models 

21.6.1. Characteristics of Pre-Trained Models 
21.6.2. Uses of Pre-Trained Models 
21.6.3. Advantages of Pre-Trained Models 

21.7. Pre-Trained Models for Transfer Learning 

21.7.1. Learning by Transfer 
21.7.2. Transfer Learning Process 
21.7.3. Advantages of Transfer Learning 

21.8. Deep Computer Vision Classification and Localization 

21.8.1. Image Classification 
21.8.2. Localization of Objects in Images 
21.8.3. Object Detection 

21.9. Object Detection and Object Tracking 

21.9.1. Object Detection Methods 
21.9.2. Object Tracking Algorithms 
21.9.3. Tracking and Localization Techniques 

21.10. Semantic Segmentation 

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

Module 22. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

22.1. Text Generation using RNN 

22.1.1. Training an RNN for Text Generation 
22.1.2. Natural Language Generation with RNN 
22.1.3. Text Generation Applications with RNN 

22.2. Training Data Set Creation 

22.2.1. Preparation of the Data for Training an RNN 
22.2.2. Storage of the Training Dataset 
22.2.3. Data Cleaning and Transformation 
22.2.4. Sentiment Analysis 

22.3. Classification of Opinions with RNN 

22.3.1. Detection of Themes in Comments 
22.3.2. Sentiment Analysis with Deep Learning Algorithms 

22.4. Encoder-Decoder Network for Neural Machine Translation 

22.4.1. Training an RNN for Machine Translation 
22.4.2. Use of an Encoder-Decoder Network for Machine Translation 
22.4.3. Improving the Accuracy of Machine Translation with RNNs 

22.5. Attention Mechanisms 

22.5.1. Application of Care Mechanisms in RNN 
22.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models 
22.5.3. Advantages of Attention Mechanisms in Neural Networks 

22.6. Transformer Models 

22.6.1. Using Transformers Models for Natural Language Processing 
22.6.2. Application of Transformers Models for Vision 
22.6.3. Advantages of Transformers Models 

22.7. Transformers for Vision 

22.7.1. Use of Transformers Models for Vision 
22.7.2. Image Data Preprocessing 
22.7.3. Training a Transformers Model for Vision 

22.8. Hugging Face Library 

22.8.1. Using the Hugging Face's Transformers Library 
22.8.2. Hugging Face’s Transformers Library Application 
22.8.3. Advantages of Hugging Face’s Transformers Library 

22.9. Other Transformers Libraries Comparison 

22.9.1. Comparison Between Different Transformers Libraries 
22.9.2. Use of the Other Transformers Libraries 
22.9.3. Advantages of the Other Transformers Libraries 

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

22.10.1. Development of a Natural Language Processing Application with RNN and Attention. 
22.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application 
22.10.3. Evaluation of the Practical Application 

Module 23. Autoencoders, GANs and diffusion models

23.1. Representation of Efficient Data 

23.1.1. Dimensionality Reduction 
23.1.2. Deep Learning 
23.1.3. Compact Representations 

23.2. PCA Realization with an Incomplete Linear Automatic Encoder 

23.2.1. Training Process 
23.2.2. Implementation in Python 
23.2.3. Use of Test Data 

23.3. Stacked Automatic Encoders 

23.3.1. Deep Neural Networks 
23.3.2. Construction of Coding Architectures 
23.3.3. Use of Regularization 

23.4. Convolutional Autoencoders 

23.4.1. Design of Convolutional Models 
23.4.2. Convolutional Model Training 
23.4.3. Results Evaluation 

23.5. Noise Suppression of Automatic Encoders 

23.5.1. Filter Application 
23.5.2. Design of Coding Models 
23.5.3. Use of Regularization Techniques 

23.6. Sparse Automatic Encoders 

23.6.1. Increasing Coding Efficiency 
23.6.2. Minimizing the Number of Parameters 
23.6.3. Using Regularization Techniques 

23.7. Variational Automatic Encoders 

23.7.1. Use of Variational Optimization 
23.7.2. Unsupervised Deep Learning 
23.7.3. Deep Latent Representations 

23.8. Generation of Fashion MNIST Images 

23.8.1. Pattern Recognition 
23.8.2. Image Generation 
23.8.3. Deep Neural Networks Training 

23.9. Generative Adversarial Networks and Diffusion Models 

23.9.1. Content Generation from Images 
23.9.2. Modeling of Data Distributions 
23.9.3. Use of Adversarial Networks 

23.10. Implementation of the Models 

23.10.1. Practical Application 
23.10.2. Implementation of the Models 
23.10.3. Use of Real Data 
23.10.4. Results Evaluation 

Module 24. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

24.1. Introduction to Bio-Inspired Computing 

24.1.1. Introduction to Bio-Inspired Computing 

24.2. Social Adaptation Algorithms 

24.2.1. Bio-Inspired Computation Based on Ant Colonies 
24.2.2. Variants of Ant Colony Algorithms 
24.2.3. Particle Cloud Computing 

24.3. Genetic Algorithms 

24.3.1. General Structure 
24.3.2. Implementations of the Major Operators 

24.4. Space Exploration-Exploitation Strategies for Genetic Algorithms 

24.4.1. CHC Algorithm 
24.4.2. Multimodal Problems 

24.5. Evolutionary Computing Models (I) 

24.5.1. Evolutionary Strategies 
24.5.2. Evolutionary Programming 
24.5.3. Algorithms Based on Differential Evolution 

24.6. Evolutionary Computation Models (II) 

24.6.1. Evolutionary Models Based on Estimation of Distributions (EDA) 
24.6.2. Genetic Programming 

24.7. Evolutionary Programming Applied to Learning Problems 

24.7.1. Rules-Based Learning 
24.7.2. Evolutionary Methods in Instance Selection Problems 

24.8. Multi-Objective Problems 

24.8.1. Concept of Dominance 
24.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems 

24.9. Neural Networks (I) 

24.9.1. Introduction to Neural Networks 
24.9.2. Practical Example with Neural Networks 

24.10. Neural Networks (II) 

24.10.1. Use Cases of Neural Networks in Medical Research 
24.10.2. Use Cases of Neural Networks in Economics 
24.10.3. Use Cases of Neural Networks in Artificial Vision 

Module 25. Artificial Intelligence: Strategies and Applications

25.1. Financial Services 

25.1.1. The Implications of Artificial Intelligence (AI) in Financial Services Opportunities and Challenges 
25.1.2. Case Uses 
25.1.3. Potential Risks Related to the Use of AI 
25.1.4. Potential Future Developments/Uses of AI 

25.2. Implications of Artificial Intelligence in the Healthcare Service 

25.2.1. Implications of AI in the Healthcare Sector Opportunities and Challenges 
25.2.2. Case Uses 

25.3. Risks Related to the Use of AI in the Health Service 

25.3.1. Potential Risks Related to the Use of AI 
25.3.2. Potential Future Developments/Uses of AI 

25.4. Retail 

25.4.1. Implications of AI in Retail. Opportunities and Challenges 
25.4.2. Case Uses 
25.4.3. Potential Risks Related to the Use of AI 
25.4.4. Potential Future Developments/Uses of AI 

25.5. Industry 

25.5.1. Implications of AI in Industry Opportunities and Challenges 
25.5.2. Case Uses 

25.6     Potential Risks Related to the Use of AI in Industry 

25.6.1. Case Uses 
25.6.2. Potential Risks Related to the Use of AI 
25.6.3. Potential Future Developments/Uses of AI 

25.7. Public Administration 

25.7.1. AI Implications for Public Administration Opportunities and Challenges 
25.7.2. Case Uses 
25.7.3. Potential Risks Related to the Use of AI 
25.7.4. Potential Future Developments/Uses of AI 

25.8. Educational 

25.8.1. AI Implications for Education Opportunities and Challenges 
25.8.2. Case Uses 
25.8.3. Potential Risks Related to the Use of AI 
25.8.4. Potential Future Developments/Uses of AI 

25.9. Forestry and Agriculture 

25.9.1. Implications of AI in Forestry and Agriculture Opportunities and Challenges 
25.9.2. Case Uses 
25.9.3. Potential Risks Related to the Use of AI 
25.9.4. Potential Future Developments/Uses of AI 

25.10 Human Resources 

25.10.1. Implications of AI for Human Resources Opportunities and Challenges 
25.10.2. Case Uses 
25.10.3. Potential Risks Related to the Use of AI 
25.10.4. Potential Future Developments/Uses of AI 

Module 26. Practical Applications of Artificial Intelligence in Design

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

26.1.1. Fundamental Concepts of Image Generation
26.1.2. Tools and Frameworks for Automatic Graphic Generation
26.1.3. Social and Cultural Impact of Generative Design
26.1.4. Current Trends in the Field and Future Developments and Applications.

26.2. Dynamic Personalization of User Interfaces Using AI

26.2.1. UI/UX Personalization Principles
26.2.2. Recommendation Algorithms in UI Customization
26.2.3. User Experience and Continuous Feedback
26.2.4. Practical Implementation in Real Applications

26.3. Generative Design: Applications in Industry and Art

26.3.1. Fundamentals of Generative Design
26.3.2. Generative Design in Industry
26.3.3. Generative Design in Contemporary Art
26.3.4. Challenges and Future Advances in Generative Design

26.4. Automatic Creation of Editorial Layouts with Algorithms

26.4.1. Principles of Automatic Editorial Layout
26.4.2. Content Distribution Algorithms
26.4.3. Optimization of Spaces and Proportions in Editorial Design
26.4.4. Automation of the Review and Adjustment Process

26.5. Procedural Generation of Content in Videogames with PCG

26.5.1. Introduction to Procedural Generation in Videogames
26.5.2. Algorithms for the Automatic Creation of Levels and Environments
26.5.3. Procedural Narrative and Branching in Videogames
26.5.4. Impact of Procedural Generation on the Player's Experience

26.6. Pattern Recognition in Logos with Using Cogniac

26.6.1. Fundamentals of Pattern Recognition in Graphic Design
26.6.2. Implementation of Machine Learning Models for Logo Identification
26.6.3. Practical Applications in Graphic Design
26.6.4. Legal and Ethical Considerations in the Recognition of Logos

26.7. Optimization of Colors and Compositions with AI

26.7.1. Color Psychology and Visual Composition
26.7.2. Color Optimization Algorithms in Graphic Design with Adobe Color Wheel and Coolors
26.7.3. Automatic Composition of Visual Elements Using Framer, Canva, and RunwayML
26.7.4. Evaluating the Impact of Automatic Optimization on User Perception

26.8. Predictive Analysis of Visual Trends in Design

26.8.1. Data Collection and Current Trends
26.8.2. Machine Learning Models for Trend Prediction
26.8.3. Implementation of Proactive Design Strategies
26.8.4. Principles in the Use of Data and Predictions in Design

26.9. AI-Assisted Collaboration in Design Teams

26.9.1. Human-AI Collaboration in Design Projects
26.9.2. Platforms and Tools for AI-assisted Collaboration (Adobe Creative Cloud and Sketch2React)
26.9.3. Best Practices in AI-Assisted Technology Integration
26.9.4. Future Perspectives on Human-AI Collaboration in Design

26.10. Strategies for the Success ful Incorporation of AI in Design

26.10.1. Identification of AI-Solvable Design Needs
26.10.2. Evaluation of Available Platforms and Tools
26.10.3. Effective Integration in Design Projects
26.10.4. Continuous Optimization and Adaptability

Module 27. Design-User Interaction and AI

27.1. Contextual Suggestions for Behavior-Based Design

27.1.1. Understanding User Behavior in Design
27.1.2. AI-Based Contextual Suggestion Systems
27.1.3. Strategies to Ensure Transparency and User Consent
27.1.4. Trends and Possible Improvements in Behavior-Based Personalization

27.2. Predictive Analysis of User Interactions

27.2.1. Importance of Predictive Analytics in User-Design Interactions
27.2.2. Machine Learning Models for Predicting User Behavior
27.2.3. Integration of Predictive Analytics in User Interface Design
27.2.4. Challenges and Dilemmas in Predictive Analytics

27.3. Adaptive Design to Different Devices with AI

27.3.1. Principles of Device Adaptive Design
27.3.2. Content Adaptation Algorithms
27.3.3. Interface Optimization for Mobile and Desktop Experiences
27.3.4. Future Developments in Adaptive Design with Emerging Technologies

27.4. Automatic Generation of Characters and Enemies in Video Games

27.4.1. The Need for Automatic Generation in the Development of Video Games
27.4.2. Algorithms for Character and Enemy Generation
27.4.3. Customization and Adaptability in Automatically Generated Characters
27.4.4. Development Experiences: Challenges and Lessons Learned

27.5. AI Improvement in Game Characters

27.5.1. Importance of Artificial Intelligence in Video Game Characters
27.5.2. Algorithms to Improve the Behavior of Characters
27.5.3. Continuous Adaptation and Learning of AI in Games
27.5.4. Technical and Creative Challenges in Character AI Improvement

27.6. Custom Design in Industry: Challenges and Opportunities

27.6.1. Transformation of Industrial Design with Personalization
27.6.2. Enabling Technologies for Customized Design
27.6.3. Challenges in Implementing Customized Design at Scale
27.6.4. Opportunities for Innovation and Competitive Differentiation

27.7. Design for Sustainability Through AI

27.7.1. Life Cycle Analysis and Traceability with Artificial Intelligence
27.7.2. Optimization of Recyclable Materials
27.7.3. Improvement of Sustainable Processes
27.7.4. Development of Practical Strategies and Projects

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

27.8.1. Role of Virtual Assistants in Interactive Design
27.8.2. Development of Virtual Assistants Specialized in Design
27.8.3. Natural Interaction with Virtual Assistants in Design Projects
27.8.4. Implementation Challenges and Continuous Improvement

27.9. Continuous User Experience Analysis for Improvement

27.9.1. Continuous Improvement Cycle in Interaction Design
27.9.2. Tools and Metrics for Continuous Analysis
27.9.3. Iteration and Adaptation in User Experience
27.9.4. Ensuring Privacy and Transparency in the Handling of Sensitive Data

27.10. Application of AI Techniques to Improve Usability

27.10.1. Intersection of AI and Usability
27.10.2. Sentiment and User Experience (UX) Analysis
27.10.3. Dynamic Interface Personalization
27.10.4. Workflow and Navigation Optimization

Module 28. Innovation in Design and AI Processes

28.1. Optimization of Manufacturing Processes with AI Simulations

28.1.1. Introduction to Manufacturing Process Optimization
28.1.2. AI Simulations for Production Optimization
28.1.3. Technical and Operational Challenges in the Implementation of AI Simulations
28.1.4. Future Perspectives: Advances in Process Optimization with AI

28.2. Virtual Prototyping: Challenges and Benefits

28.2.1. Importance of Virtual Prototyping in Design
28.2.2. Tools and Technologies for Virtual Prototyping
28.2.3. Challenges in Virtual Prototyping and Strategies for Overcoming Them
28.2.4. Impact on Design Innovation and Agility

28.3. Generative Design: Applications in Industry and Artistic Creation

28.3.1. Architecture and Urban Planning
28.3.2. Fashion and Textile Design
28.3.3. Design of Materials and Textures
28.3.4. Automation in Graphic Design

28.4. Materials and Performance Analysis Using Artificial Intelligence

28.4.1. Importance of Materials and Performance Analysis in Design
28.4.2. Artificial Intelligence Algorithms for Material Analysis
28.4.3. Impact on Design Efficiency and Sustainability
28.4.4. Implementation Challenges and Future Applications

28.5. Mass Customization in Industrial Production

28.5.1. Transformation of Production Through Mass Customization
28.5.2. Enabling Technologies for Mass Customization
28.5.3. Logistical and Scale Challenges of Mass Customization
28.5.4. Economic Impact and Innovation Opportunities

28.6. Artificial Intelligence-Assisted Design Tools (Deep Dream Generator, Fotor and Snappa) 

28.6.1. Generation-Assisted Design Gan (Generative Adversarial Networks)
28.6.2. Collective Generation of Ideas
28.6.3. Context-Aware Generation
28.6.4. Exploration of Non-Linear Creative Dimensions

28.7. Collaborative Human-Robot Design in Innovative Projects

28.7.1. Integration of Robots in Innovative Design Projects
28.7.2. Tools and Platforms for Human-Robot Collaboration (ROS, OpenAI Gym and Azure Robotics)
28.7.3. Challenges in Integrating Robots in Creative Projects
28.7.4. Future Perspectives in Collaborative Design with Emerging Technologies

28.8. Predictive Maintenance of Products: AI Approach

28.8.1. Importance of Predictive Maintenance in Product Prolongation
28.8.2. Machine Learning Models for Predictive Maintenance
28.8.3. Practical Implementation in Various Industries
28.8.4. Evaluation of the Accuracy and Effectiveness of these Models in Industrial Environments

28.9. Automatic Generation of Typefaces and Visual Styles

28.9.1. Fundamentals of Automatic Generation in Typeface Design
28.9.2. Practical Applications in Graphic Design and Visual Communication
28.9.3. AI-Assisted Collaborative Design in the Creation of Typefaces
28.9.4. Exploration of Automatic Styles and Trends

28.10. IoT Integration for Real-Time Product Monitoring

28.10.1. Transformation with the Integration of IoT in Product Design
28.10.2. Sensors and IoT Devices for Real Time Monitoring
28.10.3. Data Analysis and IoT-based Decision Making 
28.10.4. Implementation Challenges and Future Applications of IoT in Design

Module 29. Applied Design Technologies and AI 

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

29.1.1. Role of Virtual Assistants in Interactive Design
29.1.2. Development of Virtual Assistants Specialized in Design
29.1.3. Natural Interaction with Virtual Assistants in Design Projects
29.1.4. Implementation Challenges and Continuous Improvement

29.2. Automatic Detection and Correction of Visual Errors with AI

29.2.1. Importance of Automatic Visual Error Detection and Correction
29.2.2. Algorithms and Models for Visual Error Detection
29.2.3. Automatic Correction Tools in Visual Design
29.2.4. Challenges in Automatic Detection and Correction and Strategies for Overcoming Them

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

29.3.1. Analysis of Interaction Data with Machine Learning Models
29.3.2. Automated Report Generation and Recommendations
29.3.3. Virtual User Simulations for Usability Testing Using Bootpress, Botium and Rasa
29.3.4. Conversational Interface for User Feedback

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

29.4.1. Importance of Optimizing Editorial Workflows
29.4.2. Algorithms for Editorial Automation and Optimization
29.4.3. Tools and Technologies for Editorial Optimization
29.4.4. Challenges in Implementation and Continuous Improvement in Editorial Workflows

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

29.5.1. Importance of Realistic Simulations in the Videogame Industry
29.5.2. Modeling and Simulation of Realistic Elements in Video Games
29.5.3. Technologies and Tools for Realistic Simulations in Video Games
29.5.4. Technical and Creative Challenges in Realistic Video Game Simulations

29.6. Automatic Generation of Multimedia Content in Editorial Design

29.6.1. Transformation with Automatic Generation of Multimedia Content
29.6.2. Algorithms and Models for the Automatic Generation of Multimedia Content
29.6.3. Practical Applications in Publishing Projects
29.6.4. Challenges and Future Trends in the Automatic Generation of Multimedia Content

29.7. Adaptive and Predictive Design Based on User Data

29.7.1. Importance of Adaptive and Predictive Design in User Experience
29.7.2. Collection and Analysis of User Data for Adaptive Design
29.7.3. Algorithms for Adaptive and Predictive Design
29.7.4. Integration of Adaptive Design in Platforms and Applications

29.8. Integration of Algorithms in Usability Improvement

29.8.1. Segmentation and Behavioral Patterns
29.8.2. Detection of Usability Problems
29.8.3. Adaptability to Changes in User Preferences
29.8.4. Automated a/b Testing and Analysis of Results

29.9. Continuous Analysis of User Experience for Iterative Improvements

29.9.1. Importance of Continuous Feedback in Product and Service Evolution
29.9.2. Tools and Metrics for Continuous Analysis
29.9.3. Case Studies Demonstrating Substantial Improvements Achieved Through this Approach
29.9.4. Handling of Sensitive Data

29.10. AI-Assisted Collaboration in Editorial Teams

29.10.1. Transforming Collaboration in AI-Assisted Editorial Teams
29.10.2. Tools and Platforms for AI-Assisted Collaboration (Grammarly, Yoast SEO and Quillionz)
29.10.3. Development of Virtual Assistants Specialized in Editing 
29.10.4. Implementation Challenges and Future Applications of AI-Assisted Collaboration

Module 30. Ethics and Environment in Design and AI 

30.1. Environmental Impact in Industrial Design: Ethical Approach

30.1.1. Environmental Awareness in Industrial Design
30.1.2. Life Cycle Assessment and Sustainable Design
30.1.3. Ethical Challenges in Design Decisions with Environmental Impact
30.1.4. Sustainable Innovations and Future Trends

30.2. Improving Visual Accessibility in Responsive Graphic Design

30.2.1. Visual Accessibility as an Ethical Priority in Graphic Design
30.2.2. Tools and Practices for the Improvement of Visual Accessibility (Google LightHouse and Microsoft Accessibility Insights)
30.2.3. Ethical Challenges in Implementing Visual Accessibility
30.2.4. Professional Responsibility and Future Improvements in Visual Accessibility

30.3. Waste Reduction in the Design Process: Sustainable Challenges

30.3.1. Importance of Waste Reduction in Design
30.3.2. Strategies for Waste Reduction at Different Stages of Design
30.3.3. Ethical Challenges in Implementing Waste Reduction Practices
30.3.4. Corporate Commitments and Sustainable Certifications

30.4. Sentiment Analysis in Editorial Content Creation: Ethical Considerations

30.4.1. Sentiment Analysis and Ethics in Editorial Content
30.4.2. Algorithms for Sentiment Analysis and Ethical Decisions
30.4.3. Impact on Public Opinion
30.4.4. Challenges in Sentiment Analysis and Future Implications

30.5. Integration of Emotion Recognition for Immersive Experiences 

30.5.1. Ethics in the Integration of Emotion Recognition in Immersive Experiences
30.5.2. Emotion Recognition Technologies
30.5.3. Ethical Challenges in Creating Emotionally Aware Immersive Experiences
30.5.4. Future Perspectives and Ethics in the Development of Immersive Experiences

30.6. Ethics in Video Game Design: Implications and Decisions

30.6.1. Ethics and Responsibility in Videogame Design
30.6.2. Inclusion and Diversity in Video Games: Ethical Decisions
30.6.3. Microtransactions and Ethical Monetization in Videogames
30.6.4. Ethical Challenges in the Development of Narratives and Characters in Videogames

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

30.7.1. Ethical Approach to Responsible Design
30.7.2. Tools and Methods for Responsible Design
30.7.3. Ethical and Environmental Challenges in the Design Industry
30.7.4. Corporate Commitments and Responsible Design Certifications

30.8. Ethics in the Integration of AI in User Interfaces

30.8.1. Exploration of How Artificial Intelligence in User Interfaces Raises Ethical Challenges
30.8.2. Transparency and Explainability in AI Systems in User Interfaces
30.8.3. Ethical Challenges in the Collection and Use of User Interface Data
30.8.4. Future Perspectives on AI Ethics at User Interfaces

30.9. Sustainability in Design Process Innovation

30.9.1. Recognition of the Importance of Sustainability in Design Process Innovation
30.9.2. Development of Sustainable Processes and Ethical Decision-Making
30.9.3. Ethical Challenges in the Adoption of Innovative Technologies
30.9.4. Business Commitments and Sustainability Certifications in Design Processes

30.10. Ethical Aspects in the Application of Design Technologies

30.10.1. Ethical Decisions in the Selection and Application of Design Technologies
30.10.2. Ethics in the Design of User Experiences with Advanced Technologies
30.10.3. Intersections of Ethics and Technologies in Design
30.10.4. Emerging Trends and the Role of Ethics in the Future Direction of Design with Advanced Technologies

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