Checking date: 28/06/2021


Course: 2021/2022

Artificial Intelligence
(18268)
Study: Bachelor in Applied Mathematics and Computing (362)


Coordinating teacher: MOLINA LOPEZ, JOSE MANUEL

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming (Course: 1 / Semester: 1) Data Structures and Algorithms (Course: 1 / Semester: 2) Logic: (Year: 1 / Semester: 2) Discrete Mathematics: (Course: 1 / Semester: 2) Artificial Intelligence (Course: 2 / Semester: 2)
Objectives
In this course the fundamentals of Artificial Intelligence techniques will be seen from the conceptual point of view and from the practical point of view.
Skills and learning outcomes
Description of contents: programme
1. An Introduction of AI 2. Production Systems 3. Search a. Introduction b. Uninformed Search c. Heuristic Search 4. Uncertainty a. Probability calculus b. Bayesian calculus. Bayes theorem. Bayesian inference. Bayesian Networks c. Markov based models. Markov chains. Markov models. Hidden Markov Models. Markov Decision Processes (MDP). Partially observable MDPs (POMDP). d. Fuzzy logic 5. Robotics 6. Applied Artificial Intelligence
Learning activities and methodology
LEARNING ACTIVITIES AND METHDOLOGY THEORETICAL-PRACTICAL CLASSES. [44 hours with 100% classroom instruction, 1.67 ECTS] Knowledge and concepts students must acquire. Student receive course notes and will have basic reference texts to facilitate following the classes and carrying out follow up work. Students partake in exercises to resolve practical problems and participate in workshops and evaluation tests, all geared towards acquiring the necessary capabilities. TUTORING SESSIONS. [4 hours of tutoring with 100% on-site attendance, 0.15 ECTS] Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. STUDENT INDIVIDUAL WORK OR GROUP WORK [98 hours with 0 % on-site, 3.72 ECTS] WORKSHOPS AND LABORATORY SESSIONS [8 hours with 100% on site, 0.3 ECTS] FINAL EXAM. [4 hours with 100% on site, 0.15 ECTS] Global assessment of knowledge, skills and capacities acquired throughout the course. METHODOLOGIES THEORY CLASS. Classroom presentations by the teacher with IT and audiovisual support in which the subject's main concepts are developed, while providing material and bibliography to complement student learning. PRACTICAL CLASS. Resolution of practical cases and problem, posed by the teacher, and carried out individually or in a group. TUTORING SESSIONS. Individualized attendance (individual tutoring sessions) or in-group (group tutoring sessions) for students with a teacher as tutor. LABORATORY PRACTICAL SESSIONS. Applied/experimental learning/teaching in workshops and laboratories under the tutor's supervision.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • S. Russell, P. Norvig . Artificial Intelligence. Prentice Hall . 2009

The course syllabus may change due academic events or other reasons.