Base Knowledge
Basic knowledge of intelligent data analysis.
Basic knowledge of Python.
Teaching Methodologies
Theoretical classes: Presentation of new concepts and discussion of examples.
Practical classes: Implementation and testing of data analysis models for concrete problems
Learning Results
The main objective of the Data Analysis course is to present a set of techniques and methodologies based on
deep neural networks to solve real data analysis problems. Understanding the potential of these systems
and the ability to develop architectures with these characteristics will give students a set of specialized skills
to work in the area of computational data analysis. The main learning objectives of this course are:
1. Know and understand advanced concepts in the field of intelligent data analysis
2. Understand the main architectures of deep neural networks, namely convolutional networks,
recurrent networks and transformers.
3. Develop deep neural networks for application to practical problems
4. Understand the main characteristics of reinforcement learning and understand the situations in which it
should be applied
5. Use machine learning tools for the development, training and validation of data analysis models
Program
1. Deep Neural Networks
2. Convolutional networks for computer vision
3. Recurrent networks
4. Natural language processing
5. Transformers
6. Reinforcement learning
7. Generative models
8. Application of TensorFlow and Keras in the development of deep neural networks
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Mandatory
Chollet, F. (2021). Deep Learning with Python (second edition). Manning Publications.
Géron, A. (2022). Hand-On Machine Learning with Scikit-Learn & TensorFlow (third edition). O’Reilly Media Inc.
Foster, D. (2019). Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play. O’Reilly Media Inc.
Complementary
Kapoor, A., Gulli, A., Pal, S. (2022). Deep Learning with TensorFlow and Keras (third edition). Packt Publishing.
Sutton, R., Barto, A. (2018). Reinforcement Learning: An Introduction (second edition). MIT Press.
Goodfellow, I., Bengio, Y. Courville, A. (2016). Deep Learning. MIT Press.
Other resources
Support materials for theoretical and practical classes
Online resources on the topics covered
Selected scientific articles on specific topic