1. What is AI. Acting humanly. Thinking humanly. Thinking rationally. Acting rationally. 2. Foundations of AI. Philosophy. Mathematics. Economics. Neuroscience. Psychology. Computer Engineering. Control theory and cybernetics. Linguistics. 3. A brief history to AI. Current trends and challenges. Deep learning. Cognitive architectures. 4. AI approaches. 5. Pillars of AI. Knowledge representation and reasoning. Learning. Problem solving.
Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total Individual preparation of assignments 0 10,00 0 10,00 Theory class 7,00 7,00 0 14,00 Hands-on class 3,00 3,00 0 6,00 Total 10,00 20,00 0 30
Stuart Russell (2018). Stuart Russell videos. Long-Term Future of Artificial Intelligence | Artificial Intelligence (AI) Podcast.. Recuperat 03/07/2020, a https://www.youtube.com/watch?v=KsZI5oXBC0k Stuart Russell (2019). Filter Bubbles and the Future of Artificial Intelligence. Recuperat 03/07/2020, a https://www.youtube.com/watch?v=ZkV7anCPfaY Minsky, Marvin. (2006). The Emotion machine :. New York: Simon & Schuster. Catàleg Marvin Minsky (2007). Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind . Recuperat 03/07/2020, a https://web.archive.org/web/20160530104419/http://video.mit.edu/watch/emotion-machine-commonsense-thinking-artificial-intelligence-and-the-future-of-the-human-mind-9267 Pedro Domingos (2016). Ten Myths About Machine Learning. Recuperat 03/07/2020, a https://medium.com/@pedromdd/ten-myths-about-machine-learning-d888b48334a3 Tom Mitchell (2017). Key Ideas in Machine Learning. Recuperat 03/07/2020, a http://www.cs.cmu.edu/~tom/mlbook/keyIdeas.pdf Russell, Stuart J.. (2021). Artificial intelligence : (Fourth edition). Upper Saddle River: Pearson. Catàleg Kelleher, J. D., Namee, B. M., & D’Arcy, A. (2020). Machine learning for predictive data analytics. Algorithms, Worked Examples, and Case Studies (2nd Edition.). The MIT Press.
Assessment activities: Description of the activity Assessment Activity % Remediable subject Job assignment - Report about the case study on a topic selected by the student. Completeness of the report according to the instructions given. Quality of the report contents according to the criteria provided by the professor. 100 Yes
- Final grade: “Pass” or “not-Pass”. - To obtain a “Pass” grade: • You must attend 80% or more of the classes (attendance will be recorded). • You must complete an assignment that demonstrates the knowledge acquired and that will be evaluated by the faculty. Specific criteria for the "No show" grade: A student who has not attended a minimum of 80% of classes or who has not completed the assessment task is considered "Not Present". Single Assessment: Special cases will we attended according to the Doctoral School norms. Minimum requirements to pass: Obtain a Pass grade.
Tutorships will be attended under appointment (to be requested via the Moodle tools or electronic mail).
During the course sessions, the students could ask questions according to the protocol stablied by the professor (raise your hand in presential mode, icone or other alternative in virtual mode). The News forum of the course’s Moodle is encouraged as the main via for doubts and questions arisen out of the course session, so as all the students could access to the information. Individual requests will be managed by e-mail.
There is a total of three sessions, combining lecture room with lab (computer) room. When required, virtual sessions will be enabled. But in-person class is encouraged to enhance networking among the students. Lab sessions are easy to follow by persons with low skills in computer programming (tutorials, step by step).