Master’s Degree In Artificial Intelligence

In this subject, you will learn:

• Introduction to Python: features, usage, and libraries
• Numpy: objects, statistics, linear algebra
• Data structures: Pandas, series
• Data visualization: Matplotlib, graphics with Pandas
• Python for data science: datasets
• Inference with neural networks

In this subject, you will learn:

• Supervised learning algorithms: trees and rules
• Unsupervised learning algorithms: clustering, reinforcement
• Combination of classifiers
• Algorithm evaluation and metrics
• Algorithm optimization

In this subject, you will learn:

• Linear algebra: matrices, determinants, types, operations
• Study of functions of a real variable: limits, continuity, differentiation, integration
• Probability: conditional probability, Bayes’ theorem
• Bayesian statistics, Markov models
• Discrete random variables
• Continuous random variables
• Distributions and time series

In this subject, you will learn:

• Advanced neural networks
• Deep learning
• Convolutional neural networks
• Recurrent neural networks
• Generative learning: GANs (Generative Adversarial Networks)
• Deep reinforcement learning

In this subject, you will learn:

• General Data Protection Regulation (GDPR): Impact of AI on data protection regulations
• Ethics and data: Codes of ethics and manifestos
• Sustainability in AI systems and big data processing
• Responsibility associated with the creation and exploitation of AI solutions
• E-government and information society services
• National and international initiatives for ethics in Artificial Intelligence

• These seminars are held weekly during the first semester. They consist of a series of talks given by active professionals in the AI industry. Each talk will last one hour, followed by half an hour of debate and student questions. The talks will cover:
  • Entrepreneurship in AI
  • Innovation with AI
  • State of AI technology
  • Applications of AI in different industries

In this subject, you will learn:

• Introduction to natural language processing (NLP)
• Statistical models for NLP
• Distances and similarities
• Sequences, Markov, and predictions
• Neural networks for NLP
• Cloud-based NLP platforms
• Natural language generation (NLG)
• Generation tools, libraries, platforms, generative neural networks

In this subject, you will learn:

• Digital image and fundamentals of digital optics
• Definition and feature extraction
• Image recognition: classical methods
• Image recognition: deep neural networks
• Stereoscopic imaging: depth and 3D
• Image generation: Generative Adversarial Networks (GANs)
• Industrial applications of computer vision

In this subject, you will learn:

• Business, companies, investors
• Entrepreneurship and financing
• Innovation as a value generator
• Identifying market needs
• Leadership styles. Leadership in technological transformation

The student will have to develop a Master’s thesis on a chosen project. It should include the following points:

• Project selection: State of the art. Justification of project utility and innovation.
• Definition of project objectives.
• Project development.
• Final results. Conclusions and future directions.
• Communication of results and project evolution.
• Writing the project report. Citations and bibliographic references. Use of appropriate guidelines (APA).