You will have the most advanced techniques in Artificial Intelligence to diagnose diseases efficiently and early, helping to improve the quality of life of patients”

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 Global University, allowing students to establish a large network of contacts that may prove useful to them in the future.  

+100000 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 method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

Economy of Scale

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" 

ThisAdvanced master’s degree in MBA in Artificial Intelligence in Clinical Practice is comprised of 30 complete and up-to-date modules, which will offer top-quality teaching materials to provide students with a comprehensive understanding of the field. As such, the university program will include topics dedicated to algorithms, intelligent systems and machine learning. In this way, graduates will immediately apply these advanced techniques to their daily practice to enrich their projects. At the same time, the syllabus will address aspects such as neural networks, model training, deep computer vision or natural language processing.

You will go deeper into Data Mining to discover patterns or trends that are useful for the decision making process, thanks to this 100% online Advanced master’s degree”

Syllabus

TheAdvanced master’s degree in MBA in Artificial Intelligence in Clinical Practice from TECH Global University is an intensive program that prepares students to face challenges and business decisions, both nationally and internationally. Its content is designed to promote the development of organizational competencies that allow for more rigorous decision making in uncertain environments.

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

This program deals in depth with the main areas of Artificial Intelligence and is designed for managers to understand its applicability in Clinical Practice from a strategic, international and innovative perspective.

A plan designed for students, focused on their professional improvement and that prepares them to achieve excellence in the field of Artificial Intelligence in Clinical Practice. 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 Management and 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 20 Model Customization and Training with TensorFlow 
Module 21 Deep Computer Vision with Convolutional Neural Networks
Module 22 Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention   
Module 23 Autoencoders, GANs , and Diffusion Models   
Module 24 Bio-Inspired Computing
Module 25 Artificial Intelligence: Strategies and Applications 
Module 26 Diagnosis in Clinical Practice using AI 
Module 27 Treatment and Management of Patients with AI 
Module 28 Personalization of Healthcare through AI 
Module 29 Analysis of Big Data in the Health Sector with AI 
Module 30 Ethics and Regulation in Medical AI

Where, When and How is it Taught?

TECH ti offre la possibilità di svolgere questo MBA in Artificial Intelligence in Clinical Practice completamente online. Durante i 2 anni di durata della specializzazione, gli studenti potranno accedere in qualsiasi momento a tutti i contenuti di questo programma, che consentirà loro di autogestire il proprio tempo di studio. 

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 Knowledge of 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 Morals
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 Corporate Governance

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: Typology

2.2. Corporate Strategy

2.2.1. Competitive Corporate Strategy
2.2.2. Growth Strategies: Typology
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. Strategic Formulation: Process of Strategic Planning

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. 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 Management and Human Resources
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 Risks

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. Results Research

4.4. Management 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 Cash Requirements
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 Commercial Activity
4.8.5. The Company as a Facilitator of the Work of the of the State

4.9. Corporate Control Systems

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 the Control 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. Analyzing and Solving 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. Costs and Efficiency of the Operations Chain
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 Issues
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 Profitability and Efficiency of Logistics Chains

5.13. Process Management

5.13.1. Process Management
5.13.2. Process Based Focus: Process Mapping
5.13.3. Improvements in Process Management

5.14. Distribution and Transportation 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 (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 in Companies

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 Technological 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. Balanced Scorecard (BSC)

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 y 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 for 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. The Basic Elements of Marketing
7.2.3. Marketing Activities in Companies

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. Designing and Creating 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 in Launching 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 in Creating 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. Market 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. Execution Stages in Marketing Research
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, Briefing Concept 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. Operating Marketing Decisions

8.10. Promotion and Merchandising 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 Factors 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. Implementation of 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 from 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 Direction and Management:

9.5.1. Project Direction and 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. Change Management in Projects: Management of Training

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 Dialog 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 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 Big Data 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. Greedy 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. Hidden Layer
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. Adam and RMSprop Optimizers
19.3.3. Moment Optimizers

19.4. Programming 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. Model Customization and Training with TensorFlow

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. Graph Optimization with TensorFlow Operations

20.5. Loading and Preprocessing Data with TensorFlow

20.5.1. Loading Data Sets with TensorFlow
20.5.2. Preprocessing Data with TensorFlow
20.5.3. Using TensorFlow Tools for Data Manipulation

20.6. The tfdata API

20.6.1. Using the tfdata API for Data Processing
20.6.2. Construction of Data Streams with tfdata
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. Loading TFRecord Files with TensorFlow
20.7.3. Using TFRecord Files for Training Models

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 Datasetsfor 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 Application with TensorFlow

20.10.1. Practical Applications
20.10.2. Building a Deep Learning Application 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. Segmentation Methods Based on Rules

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. Rating of Reviews 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 NRN
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’s Transformers Library

22.8.1. Using 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. Trendy MNIST Image Generation

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

23.9. Generative Adversarial Networks and Dissemination 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. Bio-Inspired Computing 

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 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 Healthcare 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. Diagnosis in Clinical Practice Using AI

26.1. Technologies and Tools for AI-Assisted Diagnostics

26.1.1. Developing Software for AI-Assisted Diagnosis in Different Medical Specialties Using ChatGPT
26.1.2. Using Advanced Algorithms for Rapid and Accurate Analysis of Clinical Symptoms and Signs
26.1.3. Integration of AI into Diagnostic Devices to Improve Efficiency
26.1.4. AI Tools to Assist in the Interpretation of Laboratory Test Results Using IBM Watson Health

26.2. Integration of Multimodal Clinical Data for Diagnosis

26.2.1. AI Systems to Combine Imaging, Laboratory, and Clinical Record Data
26.2.2. Tools for Correlating Multimodal Data into More Accurate Diagnoses Using Enlitic Curie
26.2.3. Using AI to Analyze Complex Patterns from Different Types of Clinical Data
26.2.4. Integration of Genomic and Molecular Data in AI-Assisted Diagnosis

26.3. Creation and Analysis of Healthcare Datasets with AI Using Google Cloud Healthcare API

26.3.1. Developing Clinical Databases for AI Model Training
26.3.2. Using AI for the Analysis and Extraction of Insights from Large Health Datasets
26.3.3. AI Tools for Clinical Data Cleaning and Preparation
26.3.4. AI Systems for Identifying Trends and Patterns in Health Data

26.4. Visualization and Management of Health Data with AI

26.4.1. AI Tools for Interactive and Understandable Visualization of Health Data
26.4.2. AI Systems for Efficient Management of Large Volumes of Clinical Data
26.4.3. Using AI-Based Dashboards for the Monitoring of Health Indicators
26.4.4. AI Technologies for Health Data Management and Security

26.5. Pattern Recognition and Machine Learning in Clinical Diagnostics Using PathAI

26.5.1. Applying Machine Learning Techniques for Pattern Recognition in Clinical Data
26.5.2. Using AI in the Early Identification of Diseases through Pattern Analysis with PathAI
26.5.3. Developing Predictive Models for More Accurate Diagnoses
26.5.4. Implementing Machine Learning Algorithms in the Interpretation of Health Data

26.6. Interpretation of Medical Images Using AI in Research

26.6.1. AI Systems for Detection and Classification of Medical Image Anomalies
26.6.2. Using Deep Learning in the Interpretation of X-Rays, MRI and CT Scans
26.6.3. AI Tools to Improve Accuracy and Speed in Diagnostic Imaging
26.6.4. Implementing AI for Image-Based Clinical Decision Support

26.7. Natural Language Processing on Medical Records for Clinical Diagnosis using ChatGPT and Amazon Comprehend Medical

26.7.1. Use of NLP for the Extraction of Relevant Information from Medical Records
26.7.2. AI Systems for Analyzing Physician Notes and Patient Reports
26.7.3. AI Tools for Summarizing and Classifying Medical Record Information
26.7.4. Applying NLP in the Identification of Symptoms and Diagnosis from Clinical Texts

26.8. Validation and Evaluation of AI-Assisted Diagnostic Models Using ConcertAI

26.8.1. Methods for Validation and Testing of AI Models in Real Clinical Settings
26.8.2. Assessing Performance and Accuracy of AI-Assisted Diagnostic Tools
26.8.3. Using AI to Ensure Reliability and Ethics in Clinical Diagnosis
26.8.4. Implementing Continuous Assessment Protocols for AI Systems in Healthcare

26.9. AI in the Diagnosis of Rare Diseases Using Face2Gene

26.9.1. Developing AI Systems Specialized in Rare Disease Identification
26.9.2. Using AI to Analyze Atypical Patterns and Complex Symptomatology
26.9.3. AI Tools for Early and Accurate Diagnosis of Rare Diseases
26.9.4. Implementing Global Databases with AI to Improve Diagnosis of Rare Diseases

26.10. Success Stories and Challenges in AI Diagnostics Implementation

26.10.1. Analysis of Case Studies where AI has Significantly Improved Clinical Diagnosis
26.10.2. Assessment of Challenges in AI adoption in Clinical Settings
26.10.3. Discussion on Ethical and Practical Barriers in the Implementation of AI for Diagnosis
26.10.4. Examination of Strategies for Overcoming Obstacles to the Integration of AI in Medical Diagnostics

Module 27. Treatment and Management of Patients with AI 

27.1. AI-Assisted Treatment Systems

27.1.1. Developing AI Systems to Assist in Therapeutic Decision Making
27.1.2. Using AI for the Personalization of Treatments Based on Individual Profiles
27.1.3. Implementing AI Tools in the Administration of Medication Doses and Schedules
27.1.4. Integrating AI in Real-Time Monitoring and Adjustment of Treatment

27.2. Definition of Indicators for Monitoring the Patient's Health Status

27.2.1. Establishing Key Parameters Using AI to Monitor Patient Health Status
27.2.2. Using AI to Identify Predictive Indicators of Health and Disease
27.2.3. Developing Early Warning Systems Based on Health Indicators
27.2.4. Implementing AI for Continuous Assessment of Patient Health Status

27.3. Tools for Monitoring and Control of Health Indicators

27.3.1. Developing Mobile and Wearable Applications with AI for Health Monitoring and Control 
27.3.2. Implementing AI Systems for the Real-Time Analysis of Health Data
27.3.3. Using AI-Based Dashboards for Visualization and Monitoring of Health Indicators
27.3.4. Integrating IoT Devices in the Continuous Monitoring of Health Indicators with AI 

27.4. AI in the Planning and Execution of Medical Procedures with Intuitive Surgical's da Vinci Surgical System

27.4.1. Using AI Systems to Optimize the Planning of Surgeries and Medical Procedures
27.4.2. Implementing AI in the Simulation and Practice of Surgical Procedures
27.4.3. Using AI to Improve Accuracy and Efficacy in the Performance of Medical Procedures
27.4.4. Applying AI in the Coordination and Management of Surgical Resources 

27.5. Machine Learning Algorithms for the Establishment of Therapeutic Treatments

27.5.1. Using Machine Learning to Develop Personalized Treatment Protocols
27.5.2. Implementing Predictive Algorithms for the Selection of Effective Therapies
27.5.3. Developing AI Systems for Real-Time Treatment Adaptation
27.5.4. Applying AI in the Analysis of the Effectiveness of Different Therapeutic Options

27.6. Adaptability and Continuous Updating of Therapeutic Protocols Using AI with IBM Watson for Oncology

27.6.1. Implementing AI Systems for Dynamic Review and Treatment Updating
27.6.2. Using AI to Adapt Therapeutic Protocols to New Discoveries and Data
27.6.3. Developing AI Tools for Continuous Personalization of Treatments
27.6.4. Integrating AI in Adaptive Response to Evolving Patient Conditions

27.7. Optimizing Healthcare Services with AI Technology with Optum

27.7.1. Using AI to Improve the Efficiency and Quality of Healthcare Services
27.7.2. Implementing AI Systems for Healthcare Resource Management
27.7.3. Developing AI Tools for Hospital Workflow Optimization
27.7.4. Applying AI to Reduce Waiting Times and Improve Patient Care

27.8. Applying AI in Health Emergency Responses

27.8.1. Implementing AI Systems for Rapid and Efficient Health Crisis Management with BlueDot
27.8.2. Using AI to Optimize Resource Allocation in Emergency Response
27.8.3. Developing AI Tools for Disease Outbreak Prediction and Response
27.8.4. Integrating AI into Warning and Communication Systems during Health Emergencies

27.9. Interdisciplinary Collaboration in AI-Assisted Treatments

27.9.1. Encouraging Collaboration between Different Medical Specialties Using AI Systems
27.9.2. Using AI to Integrate Knowledge and Techniques from Different Disciplines into Treatment
27.9.3. Developing AI Platforms to Facilitate Interdisciplinary Communication and Coordination
27.9.4. Implementing AI in the Creation of Multidisciplinary Treatment Teams 

27.10. Successful Experiences of AI in the Treatment of Diseases

27.10.1. Analysis of Successful Cases in the Use of AI for Effective Treatment of Diseases
27.10.2. Evaluation of the Impact of AI in Improving Treatment Outcomes
27.10.3. Documentation of Innovative Experiences in the Use of AI in Different Medical Areas
27.10.4. Discussion of Advances and Challenges in the Implementation of AI in Medical Treatments 

Module 28. Personalization of Healthcare through AI

28.1. AI Applications in Genomics for Personalized Medicine with DeepGenomics
28.1.1. Development of AI Algorithms for the Analysis of Genetic Sequences and their Relationship with Diseases
28.1.2. Using AI to Identify Genetic Markers for Personalized Treatments
28.1.3. Implementing AI for Fast and Accurate Interpretation of Genomic Data
28.1.4. AI Tools in Genotype Correlation with Drug Responses

28.2. AI in Pharmacogenomics and Drug Design with AtomWise

28.2.1. Developing AI Models to Predict Drug Efficacy and Safety
28.2.2. Using AI in Therapeutic Target Identification and Drug Design
28.2.3. Applying AI in the Analysis of Gene-Drug Interactions for Treatment Customization
28.2.4. Implementing AI Algorithms to Accelerate Discovery of New Drugs

28.3. Personalized Monitoring with Smart Devices and AI

28.3.1. Development of Wearables with AI for Continuous Monitoring of Health Indicators
28.3.2. Using AI to Interpret Data Collected by Smart Devices with FitBit
28.3.3. Implementing AI-Based Early Warning Systems for Health Conditions
28.3.4. AI Tools for Customizing Lifestyle and Health Recommendations

28.4. Clinical Decision Support Systems with AI

28.4.1. Implementing AI to Assist Physicians in Clinical Decision Making with Oracle Cerner
28.4.2. Developing AI Systems that Provide Recommendations Based on Clinical Data
28.4.3. Using AI in the Assessment of Risks and Benefits of Different Therapeutic Options
28.4.4. AI Tools for Real-Time Health Data Integration and Analysis

28.5. Trends in Health Personalization with AI

28.5.1. Analyzing the Latest AI Trends for Customizing Healthcare
28.5.2. Using AI in the Development of Preventive and Predictive Approaches in Health
28.5.3. Implementing AI in Adapting Health Plans to Individual Needs
28.5.4. Exploring New AI Technologies in the Field of Personalized Health

28.6. Advances in AI-Assisted Surgical Robotics with Intuitive Surgical's da Vinci Surgical System

28.6.1. Developing AI-Enabled Surgical Robots for Precise and Minimally Invasive Procedures
28.6.2. Using AI to Create Predictive Disease Models Based on Individual Data with OncoraMedical
28.6.3. Implementing AI Systems for Surgical Planning and Simulation of Operations
28.6.4. Advances in the Integration of Tactile and Visual Feedback in Surgical Robotics with AI

28.7. Development of Predictive Models for Personalized Clinical Practice

28.7.1. Using AI to Create Predictive Disease Models Based on Individual Data
28.7.2. Implementing AI in Predicting Treatment Responses
28.7.3. Developing AI Tools for Anticipating Health Risks
28.7.4. Applying Predictive Models in Planning Preventive Interventions

28.8. AI in Personalized Pain Management and Treatment with Kaia Health

28.8.1. Developing AI Systems for Personalized Pain Assessment and Management
28.8.2. Using AI in Identifying Pain Patterns and Responses to Treatments
28.8.3. Implementing AI Tools in Customizing Pain Therapies
28.8.4. Applying AI in Monitoring and Adjusting Pain Treatment Plans

28.9. Patient Autonomy and Active Participation in Personalization

28.9.1. Promoting Patient Autonomy through AI Tools for Managing Patient Health with Ada Health
28.9.2. Developing AI Systems that Empower Patients in Decision Making
28.9.3. Using AI to Provide Personalized Information and Education to Patients
28.9.4. AI Tools that Facilitate Active Patient Participation in Their Treatment

28.10. Integration of AI in Electronic Medical Records with Oracle Cerner

28.10.1. Implementing AI for Efficient Analysis and Management of Electronic Medical Records
28.10.2. Developing AI Tools for Extracting Clinical Insights from Electronic Records
28.10.3. Using AI to Improve Accuracy and Accessibility of Data in Medical Records
28.10.4. Applying AI for the Correlation of Clinical History Data with Treatment Plans

Module 29. Analysis of Big Data in the Health Sector with AI  

29.1. Fundamentals of Big Data in Healthcare

29.1.1. The Explosion of Data in the Field of Health
29.1.2. Concept of Big Data and Main Tools
29.1.3. Applications of Big Data in Health

29.2. Text Processing and Analysis in Health Data with KNIME and Python

29.2.1. Concepts of Natural Language Processing
29.2.2. Embedding Techniques
29.2.3. Application of Natural Language Processing in Health

29.3. Advanced Methods for Data Retrieval in Health with KNIME and Python

29.3.1. Exploring Innovative Techniques for Efficient Health Data Retrieval
29.3.2. Developing Advanced Strategies for Extracting and Organizing Information in Health Settings
29.3.3. Implementing Adaptive and Customized Data Retrieval Methods for Diverse Clinical Contexts 

29.4. Quality Assessment in Health Data Analysis with KNIME and Python

29.4.1. Developing Indicators for the Rigorous Assessment of Data Quality in Health Care Settings
29.4.2. Implementing Tools and Protocols to Ensure the Quality of Data Used in Clinical Analyses
29.4.3. Continuous Assessment of Accuracy and Reliability of Results in Health Data Analysis Projects

29.5. Data Mining and Machine Learning in Health with KNIME and Python

29.5.1. Main Methodologies for Data Mining
29.5.2. Health Data Integration
29.5.3. Detection of Patterns and Anomalies in Health Data

29.6. Innovative Areas of Big Data and AI in Healthcare

29.6.1. Exploring New Frontiers in the Application of Big Data and AI to Transform the Healthcare Sector
29.6.2. Identifying Innovative Opportunities for the Integration of Big Data and AI Technologies in Medical Practices
29.6.3. Developing Cutting-Edge Approaches to Maximize the Potential of Big Data and AI in Healthcare

29.7. Medical Data Collection and Pre-Processing with KNIME and Python

29.7.1. Developing Efficient Methodologies for Medical Data Collection in Clinical and Research Settings
29.7.2. Implementing Advanced Pre-Processing Techniques to Optimize the Quality and Utility of Medical Data 
29.7.3. Designing Collection and Pre-Processing Strategies to Ensure Confidentiality and Privacy of Medical Information

29.8. Data Visualization and Communication in Healthcare with PowerBI and Python-like Tools

29.8.1. Designing Innovative Visualization Tools in Health
29.8.2. Creative Communication Strategies in Health
29.8.3. Integrating Interactive Technologies in Health 

29.9. Data Security and Governance in the Health Sector

29.9.1. Developing Comprehensive Data Security Strategies to Protect Confidentiality and Privacy in the Health Care Sector 
29.9.2. Implementing Effective Governance Frameworks to Ensure Ethical and Responsible Data Management in Medical Settings
29.9.3. Designing Policies and Procedures to Ensure the Integrity and Availability of Medical Data, Addressing Challenges Specific to the Health Sector 

29.10. Practical Applications of Big Data in Healthcare

29.10.1. Developing Specialized Solutions to Manage and Analyze Large Datasets in Healthcare Settings
29.10.2. Using Practical Big Data-Based Tools to Support Clinical Decision-Making
29.10.3. Application of Innovative Big Data Approaches to Address Specific Challenges within the Healthcare Sector

Module 30. Ethics and Regulation in Medical AI  

30.1. Ethical Principles in the Use of AI in Medicine

30.1.1. Analysis and Adoption of Ethical Principles in the Development and Use of Medical AI Systems
30.1.2. Integrating Ethical Values into AI-Assisted Decision-Making in Medical Settings
30.1.3. Establishing Ethical Guidelines to Ensure the Responsible Use of Artificial Intelligence in Medicine

30.2. Data Privacy and Consent in Medical Contexts

30.2.1. Developing Privacy Policies to Protect Sensitive Data in Medical AI Applications
30.2.2. Guarantee of Informed Consent in the Collection and Use of Personal Data in the Medical Field
30.2.3. Implementing Security Measures to Safeguard Patient Privacy in Medical AI Environments

30.3. Ethics in Research and Development of Medical AI Systems

30.3.1. Ethical Evaluation of Research Protocols in the Development of AI Systems for Health
30.3.2. Ensuring Transparency and Ethical Rigor in the Development and Validation of Medical AI Systems
30.3.3. Ethical Considerations in the Publication and Sharing of Medical AI Results30.4. Social Impact and Accountability in Health AI

30.4.    Social Impact and Accountability in Health AI

30.4.1. Analysis of the Social Impact of AI on Health Service Delivery
30.4.2. Developing Strategies to Mitigate Risks and Ethical Responsibility in Medical AI Applications
30.4.3. Continuous Social Impact Assessment and Adaptation of AI Systems to Positively Contribute to Public Health

30.5. Sustainable Development of AI in the Health Sector

30.5.1. Integration of Sustainable Practices in the Development and Maintenance of AI Systems in Health
30.5.2. Environmental and Economic Impact Assessment of AI Technologies in Health
30.5.3. Development of Sustainable Business Models to Ensure Continuity and Improvement of AI Solutions in the Health Sector

30.6. Data Governance and International Regulatory Frameworks in Medical AI

30.6.1. Development of Governance Frameworks for Ethical and Efficient Data Management in Medical AI Applications
30.6.2. Adaptation to International Regulations to Ensure Ethical and Legal Compliance
30.6.3. Active Participation in International Initiatives to Establish Ethical Standards in the Development of Medical AI Systems

30.7. Economic Aspects of AI in the Health Sector

30.7.1. Analysis of Economic Implications and Cost-Benefits in the Implementation of AI Systems in Health
30.7.2. Development of Business Models and Financing to Facilitate the Adoption of AI Technologies in the Healthcare Sector
30.7.3. Assessment of Economic Efficiency and Equity in Access to AI-Driven Health Services

30.8. Human-Centered Design of Medical AI Systems

30.8.1. Integrating Human-Centered Design Principles to Improve Usability and Acceptance of Medical AI Systems
30.8.2. Participation of Health Professionals and Patients in the Design Process to Ensure the Relevance and Effectiveness of the Solutions
30.8.3. Continuous User Experience Assessment and Feedback to Optimize Interaction with AI Systems in Medical Environments

30.9. Fairness and Transparency in Medical Machine Learning

30.9.1. Developing Medical Machine Learning Models that Promote Equity and Transparency
30.9.2. Implementing Practices to Mitigate Biases and Ensure Equity in the Application of AI Algorithms in the Field of Health
30.9.3. Continuous Assessment of Equity and Transparency in the Development and Deployment of Machine Learning Solutions in Medicine

30.10. Safety and Policy in the Implementation of AI in Medicine

30.10.1. Developing Security Policies to Protect Data Integrity and Confidentiality in Medical AI Applications
30.10.2. Implementing Safety Measures in the Deployment of AI Systems to Prevent Risks and Ensure Patient Safety
30.10.3. Continuous Evaluation of Safety Policies to Adapt to Technological Advances and New Challenges in the Implementation of AI in Medicine

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