Universidad Carlos III de Madrid
Area of Study
Computer Engineering, Computer Info Systems, Computer Programming, Computer Science, Systems Engineering
Taught In English
STUDENTS ARE EXPECTED TO HAVE COMPLETED:
Mathematics and Statistics
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Recommended U.S. Semester Credits3
Recommended U.S. Quarter Units4
Hours & Credits
Artificial Intelligence (218 - 13883)
Study: Bachelor in Informatics Engineering
Semester 2/Spring Semester
2nd Year Course/Lower Division
Students are expected to have completed:
Mathematics and Statistics
Compentences and Skills that will be Acquired and Learning Results:
- Analysis (PO a)
- Abstraction (PO a)
- Problem solving (PO c)
- Capacity to apply theoretical concepts (PO c)
1. Evaluation based on multiple Theoretical IA tasks (PO a)
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)
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: Course Description
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).
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)
Kevin Knight,B. Nair Elaine Rich. Artificial Intelligence. McGraw HIll. 2008
Stuart Russell y Peter Norvig. Artificial Intelligence: A Modern Approach. . Prentice Hall. 2009
Courses and course hours of instruction are subject to change.
ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.