Introduction to AI
Code: 106558 ECTS Credits: 3| Degree | Type | Year | Semester |
|---|---|---|---|
| 2504392 Artificial Intelligence | OB | 1 | 1 |
Contact
- Name:
- Pedro Meseguer Gonzalez
- Email:
- pedro.meseguer@uab.cat
Use of Languages
- Principal working language:
- english (eng)
- Some groups entirely in English:
- Yes
- Some groups entirely in Catalan:
- No
- Some groups entirely in Spanish:
- No
Prerequisites
No prerequisits.
Objectives and Contextualisation
En aquesta matèria s'oferirà una introducció a la Intel·ligència Artificial, per tal de donar una perspectiva històrica i general de la intel·ligència artificial així com deixar clar quina és la realitat de l'estat de l'art i les limitacions d'aquesta tecnologia amb un èmfasi en les implicacions socials.
Competences
- Communicate effectively, both orally and in writing, adequately using the necessary communicative resources and adapting to the characteristics of the situation and the audience.
- Conceive, design, analyse and implement autonomous cyber-physical agents and systems capable of interacting with other agents and/or people in open environments, taking into account collective demands and needs.
- Develop critical thinking to analyse alternatives and proposals, both one's own and those of others, in a well-founded and argued manner.
- Identify, analyse and evaluate the ethical and social impact, the human and cultural context, and the legal implications of the development of artificial intelligence and data manipulation applications in different fields.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Work independently, with responsibility and initiative, planning and managing time and available resources, and adapting to unforeseen situations.
Learning Outcomes
- Communicate effectively, both orally and in writing, adequately using the necessary communicative resources and adapting to the characteristics of the situation and the audience.
- Develop critical thinking to analyse alternatives and proposals, both one's own and those of others, in a well-founded and argued manner.
- Incorporate the principles of responsible research and innovation in AI-based developments.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Understand the social, ethical and legal implications of professional AI practice.
- Work independently, with responsibility and initiative, planning and managing time and available resources, and adapting to unforeseen situations.
Content
INTRODUCTION TO AI
Origins
The first relevant advances
AI winter
New approaches
Successful cases
Future and open problems
Ethical issues
SEARCH
Heuristic search
Combinatorial explosion
Metaheuristics
Successful cases
KNOWLEDGE REPRESENTATION - LOGIC
The role of knowledge
Logic: proof, models
Propositional and predicate logic
Limitations of logic
LEARNING
Symbolic learning
Neural learning
Deep learning
Successful cases
NATURAL LANGUAGE
Natural language tasks
Question answering
Machine translation
Successful cases
ROBOTICS
Sensors and effectors
Architectures
Service robotics
Industrial robotics
ETHICS
Ethics in engineering
Moral agents
Alignment with individual/societal values
Autonomous car dilemmas
Methodology
The sessions will be face-to-face in class and will be organized to introduce the contents of the subject, through master classes. There will also be readings of articles and a set of presentations that can be made in groups throughout the course and that will be evaluated and discussed in the face-to-face sessions. Each and every member of the group should be actively involved in the presentations, which means that the presentations should be organized among themselves.
Sessions will be organized two hours a week with all students. For the presentations, the division of the students into groups will be done at the beginning and will be fixed for the whole course. Sessions will be held in a classroom with computers to facilitate access to the Internet by the student. Students are encouraged to bring their own laptop to class if they have one.
In the face-to-face sessions, the concepts detailed in the syllabus of the subject will be worked on. In some cases, consideration will be given to making explanatory videos available to the student that the student must view prior to the class session.
The classes of problems will serve to exemplify what has been explained in the theory classes. The presentations will put students in direct contact with recent applications of AI.
The student will have to complete the face-to-face classes with the autonomous personal work in the realization of the readings and presentations that go proposed and that have to serve to finish understanding the contents of the subject. It should be borne in mind that the syllabus of the subject has a logical continuity throughout the course, so that in order to follow a class correctly it is necessary to have assimilated what has been explained in the previous sessions.
The management of the teaching will be done through the UAB Virtual Campus platform, which will be used to view the materials, manage the practice groups, make the corresponding deliveries, see the notes, communicate with the teachers, etc.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Sessions of theory, problems and presentations | 25 | 1 | 6, 1, 2, 5, 4, 3, 7 |
| Type: Autonomous | |||
| Assimilation of theory and problems sessions | 25.8 | 1.03 | 1, 2, 5, 4, 7 |
| Preparation of presentations | 20 | 0.8 | 6, 3 |
Assessment
The assessment of the subject will take into account three types of assessment activities: Two midterm exams as an individual assessment and the presentation of presentations by groups of students.
The final grade of the course is obtained by combining the assessment of these 3 activities as follows:
Final Grade = (0.6 the two partial tests of individual evaluation) + (0.4 Presentations by groups)
Presentations:
• A minimum grade of 5 in this activity must be obtained in order to pass the subject.
Individual assessment: this section includes the results of the individual tests that will be done throughout the course. There will be partial tests that will be done during the academic period of the course and a final test during the official exam period. This final test will be a recovery test and will only have to be taken by students who have not passed one of the two partial ones. If one of the two parts has been passed, but the other has not, only the part of the subject corresponding to the part that has not been passed must be retaken in this test.
• A minimum grade of 4.5 must be obtained in each of the two parts in order to pass the course.
• The final grade will be the average of the two partials:
Individual Assessment = (0.5 * Partial1) + (0.5 * Partial 2)
The assessment of the subject will take into account three types of assessment activities: Two midterm exams as an individual assessment and the presentation of presentations by groups of students.
• A minimum grade of 5 must be obtained in the grade of the individual assessment in order to pass the course.
Recovery:
• First partial: a student who fails the first partial can recover it in the final exam.
• Second partial: a student who fails the first partial can recover it in the final exam.
• Presentation / Work: in case of not reaching 5 in the presentation/work, the group has to resubmit the corrected work (obviously, there is no time to make a second presentation) in the final exam, so that it includes the instructions of the teachers.
Not assessable: A student will be considered non-assessable (NA) if he / she does not participate in the presentation and does not take any of the following assessment tests: Part 1, Part 2, Final Recovery Test.
Suspended: If the calculation of the final grade is equal to or higher than 5 but does not reach the minimum required in any of the evaluation activities, the final grade will be suspended and a 4.5 will be placed on the grade of the transcript of the student.
Honors: Granting an honors degree is the decision of the faculty responsible for the subject. UAB regulations state that MHs can only be awarded to students who have obtained a final grade equal to or higher than 9.00. Up to 5% MH of the total number of students enrolled can be awarded.
Important Note: Copies and plagiarism Without prejudice to other disciplinary measures deemed appropriate, and in accordance with current academic regulations, irregularities committed by a student that may lead to a variation in the grade will be graded with a zero (0). Assessment activities qualified in this way and by this procedure will not be recoverable. If it is necessary to pass any of these assessment activities to pass the subject, this subject will be suspended directly, without the opportunity to retake it in the same course. These irregularities include, but are not limited to: • The total or partial copy of an internship, report, or any other assessment activity • Let copy • Present a group work not done entirely by the group members • Present as own materials prepared by a third party, even if they are translations or adaptations, and in general works with non-original and exclusive elements of the student • Have communication devices (such as mobile phones, smart watches, etc.) accessibleduring individual theoretical-practical assessment tests (exams). • Talk to classmates during individual theoretical-practical assessment tests (exams); • Copying or attempting to copy other students during the theoretical-practical assessment tests (exams); • Use or attempt to use writings related to the subject during the performance of the theoretical-practical assessment tests (exams), when these have not been explicitly allowed. In these cases, the numerical grade of the transcript will be the lower value between 3.0 and the weighted average of the grades (and therefore it will not be possible to pass it by compensation). Copy of the program code will be used in the evaluation of problem and practice deliveries. Note on planning assessment activities: The dates of continuous evaluation and delivery of works will be published at the beginning of the course and may be subject to schedule changes for reasons of adaptation to possible incidents. These changes will always be reported to the Virtual Campus as it is understood that this is the usual platform for the exchange of information between teachers and students.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Presentations | 0.4 | 0.2 | 0.01 | 6, 3 |
| Test 1 | 0.3 | 2 | 0.08 | 1, 2, 5, 4, 7 |
| Test 2 | 0.3 | 2 | 0.08 | 1, 2, 5, 4, 7 |
Bibliography
Inteligencia Artificial. Ramon López de Mántaras, Pedro Meseguer, in collection “Qué Sabemos de…”, Los libros de la Catarata, 2017.
Artificial Intelligence. A modern approach. Stuart Russell, Peter Norvig. 4th edition. Pearson, 2020.
Software
None.