Neural Networks and Deep Learning

• Teachers
• Weekly hours
• Competences
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography
• Previous capacities

Teachers

Person in charge

• Luis Antonio Belanche Muñoz ( )

Others

• Anna Arias Duart ( )
• Daniel Hinjos García ( )
• Dario García Gasulla ( )

Weekly hours

Competences

Transversal Competences

Transversals

• CT5 [Avaluable] - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.

Basic

• CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.

Technical Competences

Especifics

• CE01 - To be able to solve the mathematical problems that may arise in the field of artificial intelligence. Apply knowledge from: algebra, differential and integral calculus and numerical methods; statistics and optimization.
• CE12 - To master the fundamental principles and models of computing and to know how to apply them in order to interpret, select, assess, model, and create new concepts, theories, uses and technological developments related to artificial intelligence.
• CE13 - To evaluate the computational complexity of a problem, identify algorithmic strategies that can lead to its resolution and recommend, develop and implement the one that guarantees the best performance in accordance with the established requirements.
• CE15 - To acquire, formalize and represent human knowledge in a computable form for solving problems through a computer system in any field of application, particularly those related to aspects of computing, perception and performance in intelligent environments or environments.
• CE18 - To acquire and develop computational learning techniques and to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.
• CE20 - To select and put to use techniques of statistical modeling and data analysis, assessing the quality of the models, validating and interpreting.
• CE26 - To design and apply techniques for processing and analyzing images and computer vision techniques in the area of artificial intelligence and robotics

Generic Technical Competences

Generic

• CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
• CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
• CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

Objectives

1. To know how to identify a data analysis problem and solve it from start to finish (end to end)
   Related competences: CG4, CG8, CG9, CT5, CE13, CE15,
2. To know the theoretical foundations of neural networks as models of machine learning
   Related competences: CE26, CG4, CE01, CE12, CE13, CE18, CE20,
3. To know and understand the fields of application of neural networks and know how to develop solutions to specific problems
   Related competences: CG9, CE12, CE15, CE18,
4. To know how to design solutions for problems related to language, image or sound
   Related competences: CE26, CG4, CG8, CG9, CT5, CB3, CE13, CE15, CE18,

Contents

1. General concepts of machine learning
   Review of the general theoretical concepts of machine learning. Learning as an optimization problem. Bayesian interpretation of the learning problem. Generalized linear models.
2. Foundations of artificial neural networks.
   Foundations of artificial neural networks. Basic biological concepts. McCulloch-Pitts model. Cognitive and computational implications. Lippmann networks. Loss functions, activation functions.
3. Feed-forward neural networks
   Feed-forward neural networks.
   Linear networks (I): the Perceptron.
   Linear networks (II): the Delta rule.
   Multilayer Perceptrons and Backpropagation.
   Descent of gradients and variants.
   Other optimizers: pseudo-Newton, CG, Rprop.
   Networks of radial basis functions.
   Autoencoders.
   Support vector machines.
   Convolutional networks.
   Good experimental practices.
4. Recurrent neural networks
   Recurrent neural networks. Hopfield networks.
   Bidirectional associative networks.
   Short-term memory (LSTM) networks.

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• Pattern recognition and machine learning - Bishop, Christopher M, Springer, cop. 2006. ISBN: 0387310738
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003157379706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Deep learning - Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron, MIT Press, 2016. ISBN: 9780262035613
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004107709706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Neural networks and deep learning : a textbook - Aggarwal, Charu C, Springer, 2018. ISBN: 9783319944623
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004184409706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Neural networks and learning machines - Haykin, Simon S, Prentice Hall, cop. 2009. ISBN: 9780131471399
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003533949706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Previous capacities