The students are expected to have studied Linear Systems Although not mandatory, basic knowledge on Digital Image Processing is welcome.

Learning Results and their relation with course contents - To learn digital images and the spatial filtering operation over images. - To know basic concepts of Machine Learning: loss functions, regularization, hyperparameters, data augmentation, etc. - To understand deep neural networks and their training algorithms: gradient descent and back-propagation. - To learn Convolutional Neural Networks (CNN) and their most usual processing blocks/layers. - To understand, design and train CNN architectures for image classification. - To understand, design and train advanced CNN architectures to address other task of visual recognition: object detection, image captioning, image segmentation, image synthesis, etc.

CB1: Students have demonstrated possession and understanding of knowledge in an area of study that builds on the foundation of general secondary education, and is usually at a level that, while relying on advanced textbooks, also includes some aspects that involve knowledge from the cutting edge of their field of study. CB2: Students are able to apply their knowledge to their work or vocation in a professional manner and possess the competences usually demonstrated through the development and defence of arguments and problem solving within their field of study. CG3: Knowledge of basic and technological subject areas which enable acquisition of new methods and technologies, as well as endowing the technical engineer with the versatility necessary to adapt to any new situation. ETEGISC1: Ability to construct, use and manage telecommunication networks, services, processes and applications, such as systems for capture, transport, representation, processing, storage, multimedia information presentation and management, from the point of view of transmission systems. ETEGISC6: Ability to analyze, codify, process and transmit multimedia information using analog and digital signal processing techniques.  RA1: Knowledge and Understanding. Knowledge and understanding of the general fundamentals of engineering, scientific and mathematical principles, as well as those of their branch or specialty, including some knowledge at the forefront of their field. RA3: Design. Graduates will have the ability to make engineering designs according to their level of knowledge and understanding, working as a team. Design encompasses devices, processes, methods and objects, and specifications that are broader than strictly technical, including social awareness, health and safety, environmental and commercial considerations. RA5: Applications. Graduates will have the ability to apply their knowledge and understanding to solve problems, conduct research, and design engineering devices or processes. These skills include knowledge, use and limitations of materials, computer models, process engineering, equipment, practical work, technical literature and information sources. They must be aware  of all the implications of engineering practice: ethical, environmental, commercial and industrial.

Unit 1. Basic concepts of visual recognition 1.1 Digital Images 1.2 Spatial Filtering 1.3 Part-models for object recognition Unit 2. Basic concepts of Deep learning 2.1 Machine Learning algorithms 2.2 Loss Functions 2.3 Regularization 2.4 Hyperparameters and validation 2.5 Deep Neural Networks 2.6 Gradient Decent-based learning algorithms 2.7 Backpropagation Unit 3 Convolutional Neural Networks (CNNs) for image classification 3.1 Introductions 3.2 Basic processing layers in a CNN 3.3 CNN architectures for image classification 3.4 Training a CNN for image classification: data pre-processing, data augmentation and initialization Unit 4 Deep networks for other image-related tasks: 4.1 Networks for object detection 4.2 Networks for semantic image segmentation 4.3 Networks for image synthesis: GANs, Diffusion Models, VAEs 4.4 Networks for image matching

Two teaching activities are proposed: lectures and lab sessions. LECTURES The lecture sessions will be supported by slides or by any other means to illustrate the concepts explained. In these classes the explanation will be completed with examples. In these sessions the student will acquire the basic concepts of the course. It is important to highlight that these classes require the initiative and the personal and group involvement of the students (there will be concepts that the students themselves should develop). LABORATORY SESSIONS This is a course with a high practical component, and students will attend to laboratory sessions very often. In them, the concepts explained during the lectures will be put into practice using deep learning software libraries (eg pytorch). In the laboratory, machines equipped with high-performance GPUs are available and free distributed computing systems such as Google Colab will also be used.