MASTER'S IN ARTIFICIAL INTELLIGENCE
Master's (2 years) Online

UNADE · Information Technology

MASTER'S IN ARTIFICIAL INTELLIGENCE

Program Overview

Degree Master's (2 years)
Format Online
Duration 2048 hours
Language English
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MASTER'S IN ARTIFICIAL INTELLIGENCE

The era of Artificial Intelligence (AI) is not a promise, but a reality that only those who take the initiative early can fully embrace, given its learning curve. Our Master’s Degree in Artificial Intelligence is designed for those of you who want to be part of the progress and innovation shaping our world.

Studying an Online Master’s Degree in Artificial Intelligence

Choosing to study an online Master’s Degree in Artificial Intelligence (AI) is a strategic investment in advanced education in one of today’s most dynamic and rapidly growing fields. This mode of study provides a unique opportunity to immerse yourself in the world of AI, combining the convenience and flexibility of online learning with a rigorous and up-to-date curriculum.

The online Master’s Degree in Artificial Intelligence is designed to provide students with an in-depth understanding of the fundamental concepts of AI, as well as its practical applications. Courses cover a wide range of topics, including machine learning, natural language processing, computer vision, robotics, and AI ethics. This diversity of subjects ensures that students develop a comprehensive understanding of the field.

This advanced program immerses you in the world of AI, where you will explore everything from machine learning and robotics to natural language processing and computer vision. With a strong focus on innovation and practical application, we will equip you with the tools you need to develop AI solutions capable of transforming industries, improving efficiency, and opening new horizons in research and technological development.

Through a combination of theory and hands-on projects, you will tackle real-world challenges and develop critical skills in programming, data analysis, and AI systems design. Our curriculum is designed to provide you with a deep understanding of the field and practical experience that will position you as a leader in the tech industry.

More than just an academic program, our Master’s Degree in Artificial Intelligence is a journey toward innovation and creativity. You will become part of a community of bright minds, collaborating with experts and fellow students who share your passion for technology and its potential to change the world.

Whether your interests lie in cutting-edge research, the development of innovative products, or the application of AI to solve real-world problems, our Master’s Degree in Artificial Intelligence will open the door to a world of opportunities.

International Seminars: The Perfect Complement to Your Master’s Degree

Being part of UNADE will also allow you to build an extensive professional network and discover iconic destinations. International seminars are aimed at those who wish to enhance their management skills, particularly their leadership abilities.

During the international seminars, students have the opportunity to travel to Cancún or Madrid to attend seminars and conferences focused on management and leadership skills, while also experiencing these destinations, their traditions, and their culture. All of this takes place while sharing a unique experience with professionals who have similar interests and ambitions.

This is particularly valuable for those pursuing a Master’s Degree in Artificial Intelligence, as they may have the opportunity to take on senior positions within tech companies. As a result, they will need to know how to lead and manage teams as effectively as possible.

The Master’s in Applied Artificial Intelligence is designed for professionals and recent graduates who wish to further develop their academic training and knowledge in areas such as Data Mining, Machine Learning, Natural Language Processing, among others. This programme aims to enable participants to generate new knowledge through learning and analytical practice, based on the identification of new algorithms and the application of these tools across different disciplines.

The general objective of the Master’s in Applied Artificial Intelligence is to provide students with specialized knowledge that equips them with the theoretical foundations and practical skills required to analyze, make decisions, and solve highly complex problems using fundamental tools and concepts from the field of Artificial Intelligence.

The specific objectives of this programme are to:

  • Identify the key concepts of Artificial Intelligence and their applications across different fields.

  • Understand the types of problems that can be addressed or supported by tools from the field of Artificial Intelligence.

  • Identify Machine Learning models and Natural Language Processing techniques as computational tools.

  • Identify Artificial Intelligence techniques and their applications across different areas of knowledge to support decision-making.

  • Identify Artificial Intelligence techniques and their applications across different areas of knowledge to improve organizational processes.

  • Develop the ability to analyze, synthesize, and critically evaluate information related to specific topics within the discipline of Artificial Intelligence.

  • Analyze intelligent systems that provide solutions for different functional areas within companies or organizations.

Admission & Graduate Profile

Admission Profile
Applicants should have a university degree in technology, engineering, mathematics, business, economics, or a related field. They should demonstrate analytical skills, logical reasoning, interest in Artificial Intelligence, and the ability to work autonomously and critically with information.
Graduate Profile
Graduates will be able to identify and apply Artificial Intelligence techniques, including Machine Learning and Natural Language Processing, to analyze complex problems, support decision-making, improve organizational processes, and evaluate intelligent systems in different professional contexts.

Career Opportunities

  • Artificial Intelligence Engineer.

  • Data Scientist.

  • Data Mining Specialist.

  • Intelligent Systems Software Developer.

  • Machine Learning Engineer.

  • Big Data Specialist.

  • Researcher in Evolutionary Computation.

  • Cloud Computing Systems Architect.

  • Natural Language Processing Applications Developer.

  • Virtualization Consultant.

  • Data Analyst in Research.

  • Lecturer and Researcher in Artificial Intelligence.

Study Plan

Semester 1
Course 1. Introduction to Artificial Intelligence

  • Unit 1. Introduction to Artificial Intelligence

  • Unit 2. Interaction of reasoning, logic, and knowledge acquisition

  • Unit 3. Innovations in intelligent agents

  • Unit 4. Machine learning with decision trees

Course 2. Artificial Intelligence Techniques

  • Unit 1. Foundations of artificial intelligence and search methods

  • Unit 2. Rule-based expert systems

  • Unit 3. Machine Learning decision methods

  • Unit 4. Unsupervised classification

  • Unit 5. Recommender systems

  • Unit 6. Evolutionary optimization and practical applications


Course 3. Data Mining

  • Unit 1. Data mining

  • Unit 2. Data processing

  • Unit 3. Methods for data analysis

  • Unit 4. Data exploration: patterns, ethics, and tools

Course 4. Machine Learning

  • Unit 1. Foundations of machine learning

  • Unit 2. Types of learning

  • Unit 3. Implementation

  • Unit 4. Machine learning project

Course 5. Software Engineering for Intelligent Systems

  • Unit 1. Industry 4.0 context

  • Unit 2. Intelligent robotics and artificial intelligence

  • Unit 3. Computer vision

  • Unit 4. Knowledge representation and ontologies

Semester 2
Course 6. Introduction to Big Data

  • Unit 1. What is Big Data?

  • Unit 2. Big Data concepts and opportunities

  • Unit 3. Information management in Big Data environments

  • Unit 4. Sectors for Big Data applications

Course 7. Automata and Formal Languages

  • Unit 1. What are automata and their relationship with formal languages?

  • Unit 2. Deterministic and nondeterministic finite automata

  • Unit 3. Regular expressions

  • Unit 4. Turing machines


Course 8. Evolutionary Computation

  • Unit 1. Historical background

  • Unit 2. Heuristic techniques

  • Unit 3. Main paradigms

  • Unit 4. Evolutionary computation in the context of artificial intelligence


Course 9. Neural Networks

  • Unit 1. Introduction to neural networks

  • Unit 2. Neural models

  • Unit 3. Paradigms

  • Unit 4. Supervised learning and neural networks

Course 10. Virtualization and Cloud Computing

  • Unit 1. Introduction to cloud computing

  • Unit 2. Cloud computing applied to business management

  • Unit 3. VMware vSphere product suite

  • Unit 4. Cloud server virtualization

  • Unit 5. Application virtualization

Semester 3
Course 11. Natural Language Processing

  • Unit 1. From human language to code: the NLP revolution

  • Unit 2. Applications of natural language processing

  • Unit 3. Lexical analysis

  • Unit 4. Syntactic analysis

Course 12. Research Preparation

  • Unit 1. Scientific research.

  • Unit 2. Types of research and research designs.

  • Unit 3. Research methods.

  • Unit 4. Research techniques.

  • Unit 5. Problem definition and development of the theoretical framework.

  • Unit 6. Hypothesis formulation and sample selection.

  • Unit 7. Data collection and data analysis.

  • Unit 8. Preparation of a research project.

  • Unit 9. Formal and structural aspects of a doctoral dissertation.

Semester 4
Course 13. Degree Completion Seminar

Academic Itinerary

Semester 1
Course 1. Introduction to Artificial Intelligence

  • Unit 1. Introduction to Artificial Intelligence

  • Unit 2. Interaction of reasoning, logic, and knowledge acquisition

  • Unit 3. Innovations in intelligent agents

  • Unit 4. Machine learning with decision trees

Course 2. Artificial Intelligence Techniques

  • Unit 1. Foundations of artificial intelligence and search methods

  • Unit 2. Rule-based expert systems

  • Unit 3. Machine Learning decision methods

  • Unit 4. Unsupervised classification

  • Unit 5. Recommender systems

  • Unit 6. Evolutionary optimization and practical applications


Course 3. Data Mining

  • Unit 1. Data mining

  • Unit 2. Data processing

  • Unit 3. Methods for data analysis

  • Unit 4. Data exploration: patterns, ethics, and tools

Course 4. Machine Learning

  • Unit 1. Foundations of machine learning

  • Unit 2. Types of learning

  • Unit 3. Implementation

  • Unit 4. Machine learning project

Course 5. Software Engineering for Intelligent Systems

  • Unit 1. Industry 4.0 context

  • Unit 2. Intelligent robotics and artificial intelligence

  • Unit 3. Computer vision

  • Unit 4. Knowledge representation and ontologies

Semester 2
Course 6. Introduction to Big Data

  • Unit 1. What is Big Data?

  • Unit 2. Big Data concepts and opportunities

  • Unit 3. Information management in Big Data environments

  • Unit 4. Sectors for Big Data applications

Course 7. Automata and Formal Languages

  • Unit 1. What are automata and their relationship with formal languages?

  • Unit 2. Deterministic and nondeterministic finite automata

  • Unit 3. Regular expressions

  • Unit 4. Turing machines


Course 8. Evolutionary Computation

  • Unit 1. Historical background

  • Unit 2. Heuristic techniques

  • Unit 3. Main paradigms

  • Unit 4. Evolutionary computation in the context of artificial intelligence


Course 9. Neural Networks

  • Unit 1. Introduction to neural networks

  • Unit 2. Neural models

  • Unit 3. Paradigms

  • Unit 4. Supervised learning and neural networks

Course 10. Virtualization and Cloud Computing

  • Unit 1. Introduction to cloud computing

  • Unit 2. Cloud computing applied to business management

  • Unit 3. VMware vSphere product suite

  • Unit 4. Cloud server virtualization

  • Unit 5. Application virtualization

Semester 3

Course 11. Natural Language Processing

  • Unit 1. From human language to code: the NLP revolution

  • Unit 2. Applications of natural language processing

  • Unit 3. Lexical analysis

  • Unit 4. Syntactic analysis

Course 12. Research Preparation

  • Unit 1. Scientific research.

  • Unit 2. Types of research and research designs.

  • Unit 3. Research methods.

  • Unit 4. Research techniques.

  • Unit 5. Problem definition and development of the theoretical framework.

  • Unit 6. Hypothesis formulation and sample selection.

  • Unit 7. Data collection and data analysis.

  • Unit 8. Preparation of a research project.

  • Unit 9. Formal and structural aspects of a doctoral dissertation.

Semester 4
Course 13. Degree Completion Seminar

Research Lines

Research Area 1. Trends in Artificial Intelligence.

Research Area 2. Applications of Artificial Intelligence.

Research Area 3. Foresight in Artificial Intelligence.

Research Area 4. Intelligent systems.

Research Area 5. Artificial Intelligence applied to education.

Research Area 6. Artificial Intelligence and healthcare.

Research Area 7. Artificial Intelligence in companies and organizations.

Research Area 8. Artificial Intelligence and information systems.

Research Area 9. Artificial Intelligence and Soft Computing.

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