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The Master in Artificial Intelligence and Deep Learning provides a sound understanding of the principles, tools and implications of artificial systems capable of sensing, understanding and decision making and prepares students to build applications in diverse areas such as arts, humanities, sciences and business.

Master in Artificial Intelligence and Deep Learning

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Master in Artificial Intelligence and Deep Learning

The Master in Artificial Intelligence and Deep Learning provides the tools to understand how Machine Intelligence works as well as to put into perspective the impact of artificial sensing, cognition and action in areas such as Finance, Medicine of Arts.

The objetives of the Master in Artificial Intelligence and Deep Learning consist on:

  • Understanding the formal foundations of Machine Learning and its implications in human-machine interactions.
  • Learning how to use high level languages in order to develop real applications based on AI as well as understanding the problems in implementing such applications in practice.
  • Guiding the proposal of AI-based solutions, considering the ethical and legal aspects and the economic and social implications.

Master in Artificial Intelligence and Deep Learning - Universidad of Alcalá - UAH
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What do we offer?

Master in Artificial Intelligence and Deep Learning

TRAINING COMPATIBLE WITH YOUR WORK

Our Master in Artificial Intelligence and Deep Learning allows you to make your training COMPATIBLE WITH YOUR WORK thanks to an innovative learning methodology.

SOUND PREPARATION

A SOUND PREPARATION for a total of 60 ECTS credits, which allows to cover in depth all the concepts and applications.

METHODOLOGY

A METHODOLOGY focussed on practice and context, using cases, real situations and technological tools that allow you to learn from the beginning.

UPDATED CURRICULUM

An UPDATED CURRICULUM, which ensures that our students are obtaining the latest knowledge in line with the trends and demands of Society.

OUTSTANDING PROFESSORS

We have arranged a team of OUTSTANDING PROFESSORS with hands-on experience in the design of Artificial Intelligence and Deep Learning Systems.

COMMUNITY AND ENVIRONMENT

We develop a COMMUNITY AND ENVIRONMENT that allows students to keep in contact with the professional sector, with seminars and activities to enrich your training experience.

PROFESSIONAL DEVELOPMENT PLAN

We will help you to design your PROFESSIONAL DEVELOPMENT PLAN to exploit at its most your talent and knowledge.

UNIVERSITY OF ALCALÁ

Study your program at the UNIVERSITY OF ALCALA, one of the best Universities in Europe.
Candidate profile.

Master in Artificial Intelligence and Deep Learning

The Master in Artificial Intelligence and Deep Learning targets both professionals and young graduates interested on understanding the foundations and implications of Artificial Intelligence and Deep Learning in Business, Art and Society.

The profile of candidates that can apply is: young graduates in engineering, medicine, social sciences or professionals with a graduate degree.

Program.

Master in Artificial Intelligence and Deep Learning

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

• History and Evolution of Artificial Intelligence. • Supervised, unsupervised and reinforced Learning • Symbolic and sub-symbolic learning. • Classification and Regression Models. • Model Optimization

FEEDFORWARD NETWORKS

• Feed-Forward single-layer networks. • Multilayer Networks. • Backpropagation Algorithm. • Loss functions. • Hyper-parameters and learning strategies.

GENETIC ALGORTHMS AND EVOLUTIONARY COMPUTATION

• Search and Optimization. • Coding. • Genetic Algorithms • Evolutionary Strategies. • Swarm Models.

UNSUPERVISED AND REINFORCED LEARNING

• Clustering and Classification. • K-Mean type Algorithms. • NN-type algorithms • Tree Algorithms. • Reinforcement learning

PROGRAMING IN PYTHON

• Arrays, matrices and vectors • Graphics • Program flow management • Interfaces and data loading • Programming exercises

CONVOLUTIONAL AND SEQUENTIAL NETWORKS

• Fundamentals and structure of convolutional networks. • Residual networks. • Sequential and time series problems. • Recurrent networks. • Backpropagation through time • LSTM models.

SEMINARS

• Seminars on applications of Deep Learning to the fields of medicine, finance, automotive driving, computer vision, speech recognition and others.

AUGMENTED INTELLIGENCE AND HUMAN MACHINE INTERACTION

• Cognitive Theories. • Interaction design. • Robot ethics. • Augmentation technologies

MASTER’S THESIS

Lecturers.

The professors of the Master in Artificial Intelligence and Deep Learning are a group of professionals with hands-on experience in the application of Artificial Intelligence and Deep Learning.

Tenured Associate Professor of Economics (Finance) and of Computer Science (Artificial Intelligence). Fulbright Visiting Scholar in several US Universities and consultant for several Spanish Fortune 500 companies and for the Government. He has authored more than one hundred publications and has lectured in the USA, Asia and Latin America. Director of the Laboratory of Computational Finance and Head of the Chair in Big Data and Predictive Analytics in Banking of the University of Alcalá.
Ignacio Olmeda
Master's Director

Head of Artificial Intelligence at BBVA. He was previously Head of Data Science at Sanitas and Head of Insights at Telefonica> He also worked for Ericsson both in Spain and Germany. He holds an MS in Computer Science, an MS in Computer Science, Systems Networking and Telecommunications and is currently pursuing his Ph.D. He has several patents and is the author of a number of publications in IT and AI.
Jesus Renero
Lecturer

He is the Artificial Intelligence Manager at Everis. He was previously Big Data and Analytics Manager at Altran. He has a long teaching experience in Mathematics and Data Science for Civil Engineering and has published a number of reserach papers in logistics and AI. He holds a BS in Mathematics, an MS in Computer Science, an MS in Transport Science and Logistics and a Ph.D. in Computer Science.
Francisco Soler
Lecturer

Daniel is Coordinator at the Laboratory of Computational Finance. His background includes Computer Science courses at the University of Alcalá and several other professional courses. He is a seasoned programmer in databases, algorithms and networks. He has worked in more than twenty projects in the application of IT in the areas of Finance, Tourism and Environmental Sciences for companies such as Repsol, Telefonica and Santander.
Daniel González López
Lecturer
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