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Educational guide | |||||||||||||||||||||||||||||||||||||||
IDENTIFYING DATA | 2024_25 | |||||||||||||||||||||||||||||||||||||||
Subject | ARTIFICIAL INTELLIGENCE AND NANOTECHNOLOGY | Code | 00707038 | |||||||||||||||||||||||||||||||||||||
Study programme |
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Descriptors | Credit. | Type | Year | Period | ||||||||||||||||||||||||||||||||||||
6 | Optional | Fourth | First |
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Language |
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Prerequisites | ||||||||||||||||||||||||||||||||||||||||
Department | ING.ELECTR.DE SIST. Y AUTOMATI |
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Coordinador |
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halam@unileon.es mcbenc@unileon.es mgaro@unileon.es |
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Lecturers |
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Web | http://agora.unileon.es | |||||||||||||||||||||||||||||||||||||||
General description | ||||||||||||||||||||||||||||||||||||||||
Tribunales de Revisión |
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Competencias |
Code | |
A18676 | |
B5653 | |
B5655 | |
B5665 | |
C2 | CMECES2 That students know how to apply their knowledge to their work or vocation in a professional manner and possess the skills that are usually demonstrated through the development and defense of arguments and the resolution of problems within their area of study. |
C3 | CMECES3 That students have the ability to gather and interpret relevant data (normally within their area of study) to make judgments that include reflection on relevant issues of a social, scientific or ethical nature. |
C5 | CMECES5 That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy |
Learning aims |
Competences | |||
Understanding artificial intelligence techniques and their application in different fields of engineering | A18676 |
B5653 |
C3 |
Knowing how to apply AI and nanotechnology concepts in engineering when solving problems. | A18676 |
B5655 B5665 |
C5 |
Having the ability to learn autonomously when solving problems | B5653 B5655 |
C2 C3 C5 |
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Develop the learning skills necessary to undertake further studies. | B5653 B5655 |
C5 |
Contents |
Topic | Sub-topic |
BLOCK I: INTRODUCTION | Topic 1: HISTORY AND BASIC CONCEPTS. Historical introduction to artificial intelligence. Applications. Sensors. Biological model. Learning. |
BLOCK II: AI APPLIED TO INDUSTRIAL ENGINEERING | Topic 1: FUZZY LOGIC. History, concepts, and implementation. Topic 2: GENETIC ALGORITHMS. History, concepts, and implementation. Topic 3: EXPERT AND KNOWLEDGE-BASED SYSTEMS. History, concepts, architecture, knowledge representation, and implementation. Topic 4: MACHINE LEARNING. History, concepts, algorithms and techniques, deep learning, and implementation |
BLOCK III: NANOTENCHNOLOGY | Topic 1: INTRODUCTION. History, general concepts. Applications in the industrial field. Topic 2: INTRODUCTION TO QUANTIC DEVICES. Basic concepts of quantum physics and simulation of devices based on quantum wells. |
Planning |
Methodologies :: Tests | |||||||||
Class hours | Hours outside the classroom | Total hours | |||||||
Laboratory practicals | 30 | 39 | 69 | ||||||
Personal tuition | 2 | 0 | 2 | ||||||
Presentations / expositions | 5 | 5 | 10 | ||||||
PBL (Problem Based Learning) | 20 | 12 | 32 | ||||||
Lecture | 14 | 21 | 35 | ||||||
Mixed tests | 2 | 0 | 2 | ||||||
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Description | |
Laboratory practicals | Practical realization of the contents covered in the master sessions. |
Personal tuition | Time that each teacher has reserved to answer and resolve students' doubts. |
Presentations / expositions | Oral presentation by students of a specific topic or a work. |
PBL (Problem Based Learning) | Strategy consisting of problem solving and reflection on their experiences that students must carry out, normally working collaboratively. |
Lecture | Presentation of the contents of the subject |
Personalized attention |
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Assessment |
Description | Qualification | ||
Laboratory practicals | Delivery of practices where the concepts acquired in the Problem Based Learning (PBL)/Problem Based Learning (PBL) part will be applied. Their defense will be carried out in the form of a practical computer test with a value of 40% plus a practical defense of a genetic algorithm mini project with a value of 10%. | 50% | |
Lecture | Evaluation of the contents acquired in the theory master sessions based on mixed learning environment tests 35% and interactive questionnaires 5%. | 40% | |
Presentations / expositions | Defense of the work on the proposed topic in groups where students will have to demonstrate knowledge of the domain in which the theoretical/practical work is developed. | 10% | |
Others | Attendance, use and active participation will be positively valued. To qualify for continuous evaluation, you must obtain a minimum of 30% in each of the previous sections. |
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Other comments and second call | |||
In the extraordinary call, a test will be carried out that evaluates the theoretical and practical skills acquired by the student. EVALUATION TESTS During the evaluation tests, it will not be possible to use any material or device that has not been expressly authorized by the teacher. If any irregularity occurs during the corresponding exam or evaluation test, the exam will be immediately withdrawn, the student will be expelled and graded as a fail. In any case, the provisions of the internal regulations of the ULE included in the document "Guidelines for action in cases of plagiarism, copying or fraud in exams or evaluation tests" will be followed (Approved Permanent Commission of the Government Council 01/29 /2015). |
Sources of information |
Access to Recommended Bibliography in the Catalog ULE |
Basic |
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Complementary | |
Recommendations |
Other comments | |
Basic knowledge in MATLAB and other programming languages. |