Universidad Carlos III de Madrid

Artificial Intelligence


Iowa State Course Substitution

Principles of Artificial Intelligence

COM S 472

Course Info

International Credits: 6.0
Converted Credits: 3.5
Semester: spring
Country: Spain
Language: English
Course Description:
Artificial Intelligence Department assigned to the subject: Department of Computer Science and Engineering Type: Compulsory ECTS Credits : 6.0 Year : 2 Semester : 2 Coordinating teacher: MOLINA LOPEZ, JOSE MANUEL Academic Year: ( 2017 / 2018 ) Review date: 28-04-2017 STUDENTS ARE EXPECTED TO HAVE COMPLETED Mathematics and Statistics COMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS. General competences: - Analysis (PO a) - Abstraction (PO a) - Problem solving (PO c) - Capacity to apply theoretical concepts (PO c) Specific competences - Cognitive 1. Evaluation based on multiple Theoretical IA tasks (PO a) - Procedimental/Instrumental 2. Students should use different IA techniques, compare them through experiments, and analyze the results (PO b) 3. Students should apply the right and appropriate AI technique and parameters to solve a task (objective) (PO c) - Attitudinal 4. Students should work on the homeworks in teams (PO d) 5. Students are required to use AI tools and provide solutions to real-world problems through computer engineering (PO e) 6. Students must present a written summary for each homework, the final homework should be orally presented, and the final exam is written (PO g) 7. Students should be able to use state of the art AI tools to solve homework tasks (PO k) DESCRIPTION OF CONTENTS: PROGRAMME 1. An Introduction of AI 2. Representation I. Introduction 3. Representation II. Production Systems 4. Search I. Introduction 5. Search II. Blind 6. Search III. Heuristic 7. Reasoning under Uncertainty I. Introduction. 8. Reasoning under Uncertainty II. Bayesian INference. 9. Reasoning under Uncertainty III. Bayesian Networks. 10. Reasoning under Uncertainty IV. Markov Models. 11. Reasoning under Uncertainty V. Fuzzy Logic I. 12. Reasoning under Uncertainty VI. Fuzzy Logic II. 13. Applied Artificial Intelligence I 14. Applied Artificial Intelligence II LEARNING ACTIVITIES AND METHODOLOGY Theoretical lectures: 2 ECTS. To achieve the specific cognitive competences of the course (PO a). Practical lectures: 2,5 ECTS. To develop the specific instrumental competences and most of the general competences, such as analysis, abstraction, problem solving and capacity to apply theoretical concepts. Besides, to develop the specific attitudinal competences. (PO a, c, d, f, g). Guided academic activities (present teacher): 1,5 ECTS. The student proposes a project according to the teachers guidance to go deeply into some aspect of the course, followed by public presentation (PO a, c, d, g, k). Página 1 de 2 ASSESSMENT SYSTEM Exercises and examinations are both learning and evaluation activities. The evaluation system includes the assessment of guided academic activities and practical cases, with the following weights: Examination: 40% (PO a) Exercises: 30% (PO b, c, d, e) Practical case: 30% (PO a, c, d, g) % end-of-term-examination: 40 % of continuous assessment (assigments, laboratory, practicals…): 60 BASIC BIBLIOGRAPHY - Kevin Knight,B. Nair Elaine Rich Artificial Intelligence, McGraw HIll, 2008 - Stuart Russell y Peter Norvig Artificial Intelligence: A Modern Approach. , Prentice Hall, 2009


Evaluation Date:
February 9, 2018
Jin Tian
This is an appropriate substitution for Com S 472.