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
About evaluation:
50% Exam.
50% Lab's evaluation (Every exercise Mark >= 4/10)
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Use of Artificial Intelligence (AI) in Laboratory Work
Artificial Intelligence (AI) tools may be used to support your learning during this course, but their use is subject to the following rules.
AI may be used to:
- Clarify theoretical concepts related to the laboratory.
- Explain scientific or engineering principles.
- Improve the grammar, spelling, and readability of laboratory reports.
- Help understand feedback provided by the instructors.
AI must not be used to:
- Write or generate code, scripts, or programs for the laboratory assignments.
- Solve the laboratory exercises on your behalf.
- Generate data, results, analyses, or conclusions that are presented as your own work.
All code submitted in this course must be written by the student unless explicitly stated otherwise by the instructor. Students are expected to understand, design, implement, and debug their own solutions.
If AI is used for any permitted purpose (e.g., language editing or conceptual clarification), its use should be acknowledged briefly in the submitted report.
Students remain fully responsible for the accuracy, originality, and integrity of all submitted work. Misrepresenting AI-generated content as one's own work constitutes academic misconduct and will be handled according to the University's academic integrity regulations.
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During examinations
Students must enter the classroom where the assessment activity is to take place with all communication devices — mobile phones, computers, tablets, smartwatches, etc. — switched off and kept inside their backpacks/bags. Failure to comply with this rule will result in a grade of 0 for the activity, as well as the implementation of the actions described in Article 21 of the UdG regulations governing student assessment and grading processes.
If, during the correction process of the assessment activity, the lecturer determines the existence of possible fraud, they reserve the right to validate the grade obtained using the assessment methodology they deem appropriate.
Criteris específics de la nota «No Presentat»:
Lab assignments are mandatory. Failure to deliver a lab assignment implies that the student will not be evaluated in the module.
Avaluació única:
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.
Requisits mínims per aprovar:
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.