MASTER'S IN APPLIED ARTIFICIAL INTELLIGENCE

Maestría 1 año En línea

UNADE · Informatica

MASTER'S IN APPLIED ARTIFICIAL INTELLIGENCE

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Titulación Maestría 1 año

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Acreditaciones y Reconocimientos

UNADE OFICIAL

The Master’s Degree 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 Degree 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.

Salidas Profesionales

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.

Plan de Estudios

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

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

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.

Course 13. Degree Completion Seminar

Research Areas:

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.

Itinerario Académico

Semestre 1