CG1 Organize and evaluate the learning and the research activity themselves and develop strategies to improve them. CG1- Organize and evaluate the learning and the research activity themselves and develop strategies to improve them CB6 Possess and understand the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context. CB6- Possess and understand the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB8 That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments. CB8- That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments CB10 That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous. CB10- That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous CE1 Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics. CE1- Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics CE2 Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it. CE2- Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it CE3 Understand, develop, modify and effectively apply machine learning methods. CE3- Understand, develop, modify and effectively apply machine learning methods. CE8 Understand the mathematical foundations of intelligent robotic system algorithms. CE8- Understand the mathematical foundations of intelligent robotic system algorithms
Apply techniques of modelling and calibrating computer vision systems. Compute 3D information of the real world from 2D image projections Apply the principles of triangulation, stereovision, and multicamera geometry Understand the limitations of some feature detecting and feature matching algorithms and how to remove false data associations Basic working knowledge of Structure-from-Motion and Visual Odometry Building creative proposals
1. Introduction 2. Linear prediction: Regression 3. Logistic Regression 4. Support Vector Machines 5. Decision Trees 6. Unsupervised learning: K-means Clustering 7. Unsupervised learning: PCA 8. Neural Networks 9. Convolutional Neural Networks
Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total Assessment test 4,00 8,00 0 12,00 Seminars 10,00 20,00 0 30,00 Theory class 18,00 36,00 0 54,00 Hands-on class 20,00 34,00 0 54,00 Total 52,00 98,00 0 150
Assessment activities: Description of the activity Assessment Activity % Remediable subject Lab assignments These lab assignments are evaluated on the basis of the Matlab code delivered by the students and a report explaining what has been done, what problems have aroused, and how the students have solved these problems. 50 Yes Exam Coherence of answers with respect to the reviewed contents. Synthesis ability. 50 No
About evaluation: 50% Exam. 50% Lab's evaluation (Every exercise Mark >= 4/10) Specific criteria for the "No show" grade: Lab assignments are mandatory. Failure to deliver a lab assignment implies that the student will not be evaluated in the module. Single Assessment: Exam of theoretical and practical contents of the subject. In order to be able to do this, it will be necessary to first deliver two alternative labs that will be provided to students who opt for the single assessment. The final grade will be 80% of the exam and 20% of the labs. If deemed necessary, a meeting will be organized where teachers can ask questions they deem appropriate about the lab reports delivered. For the students to be elegible for the single assessment, they should apply within the deadlines set and in accordance with the procedures and criteria established by the Governing Board of the center. Minimum requirements to pass: To pass the module, the global mark must be >= 5/10
Students can arrange tutorial sessions with the professor by contacting the professor via email. Whenever possible, questions and doubts will be solved via email. Otherwise the tutorial will be conducted using Zoom or face-to-face.
Presentation of information about the course and course activities will be done through Moodle. Zoom or Teams will be used for non-contact sessions. All message communication between the professors and the students will be made by internal Moodle messaging system or by email. Students will use Moodle to upload reports.