Descriptive summary of the curricular unit Although it sometimes goes unnoticed, artificial intelligence (AI) is an increasingly present reality. And, contrary to what some science fiction movies might have us believe, this technology has the function of facilitating operations, bringing more agility and quality of life.
Objectives and expected learning outcomes Artificial Intelligence and its associated aspects - computational intelligence, robots, intelligent systems and artificial life ¿ are increasingly present areas in current technological development with immense potential for the near future. The overall objective of this curricular unit is the study of fundamental concepts and techniques related to AI applied to business.
Skills to be developed Students are expected to acquire knowledge and skills in each of the following topics: P1. Introduction to artificial intelligence; P2. Troubleshooting with Search and Optimization; P3. Knowledge Representation and Reasoning; P4. Learning, Feedback and Relationship Machine.
Programme
C1. Introduction to Artificial Intelligence and Intelligent Systems; C2. Troubleshooting with Search and Optimization; C3. Genetic algorithms; C4. Particle Swarm Optimization Algorithm; C5. Data grouping; C6. Introduction to Fuzzy Logic; C7. Introduction to Agents Case-Based Reasoning; C8. Rule Based Reasoning; C9. Introduction to Neuronal Networks; C10. Introduction to Vector Support Machines; C11. Decision trees; C12. Random Forests
Demonstration of the syllabus coherence with the curricular unit's learning objectives
P1. Introduction to Artificial Intelligence (C1; C2; C3) P2. Troubleshooting with Search and Optimization (C4; C5; C6) P3. Knowledge Representation (C7; C8; C9) P4. Learning, Feedback, and Relationship Machine (C10; C11; C12)
Main literature
John K. Thompson and Douglas B. Laney;Building Analytics Teams: Harnessing Analytics and Artificial Intelligence for Business Improvement, Packt Publishing Ltd., 2020. ISBN: 1800203160
Rajendra Akerkar;Artificial intelligence for business, Springer, 2019
Supplementary Bibliography
Wolfgang Amann and ¿Agata Stachowicz-Stanusch ;Artificial Intelligence and its Impact on Business, Information Age Publishing, 2020
Jim Sterne;Artificial intelligence for marketing: practical applications, John Wiley & Sons, 2017
Learning Methods
Theoretical classes present the topics contained in the syllabus, interspersing them with the resolution of some framing exercises; In practical classes students do two individual or group assignments. The completion of the work also requires a research of the state of the art topics, motivating the student and promoting the self-learning component.
Assessment Components
Avaliação distribuída com exame final
Assessment Components
Description
Type
Time (hours)
Conclusion Date
Attendance (estimated)
Lessons
45
Teste/Exame
3
Projectos
3
Trabalho laboratorial ou de campo
40
Participação Presencial
3
Participação Presencial
9
Study
57
Total:
160
Continuous Assessment
Continuous assessment also foresees that the Practical Assignment component has a minimum grade of 9.5 out of 20 to access a Final Test, with a minimum grade of 9.5 out of20, weighing 40% of the final grade of the course unit.
Under the General Regulation:
a) The effective attendance of students in class will be recorded and, if the number of absences per student exceeds 30% of the total number of contact sessions for each course unit, will be automatically transferred to the final evaluation of the normal season; b) In the written tests and in the defined evaluation elements it is necessary to obtain a minimum grade of 7.5 points; c) If the student misses or achieves a grade lower than 7.5 points in the tests or evaluation elements referred to in the previous number, he / she will be automatically transferred to the final evaluation of the normal season; d) If the student misses or achieves a grade lower than 7.5 points in the second written test (held on the same date as the final written test of the normal season), he / she may require registration for evaluation at the time of appeal; e) All written academic work provided for in the assessment (reports, case studies, etc.) must be submitted to the Turnitin database, available on the ISAG E-Learning platform, with a similarity rate up to 30% acceptable.
Final Exam
Assessment by Final Assessment provides a global exam of the course unit with a minimum grade of 9.5 out of 20 and a weight of 100% of the unit grade.
Proofs and special works
E-learning group activity (20% of the total grade)
2 individual tests (40% each)
Demonstration of the coherence between the teaching methodologies and the learning outcomes
The teaching methodology aims to promote the development of competence in the area of the curricular unit, namely by leading students to carry out practical work, as well as develop research skills and synthesis of content applied to Tourism and Hotel Management. This methodology is in accordance with the objectives of the curricular unit: ET = Theoretical Teaching: Presentation, discussion and exemplification of the fundamental concepts of Artificial Intelligence (C1 to C12); EP = Practical Teaching: Problem Solving and Case Studies (C2, C12)