| Subject name (in Hungarian, in English) | Introduction of artificial intelligence | |||
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Introduction to artificial intelligence
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| Neptun code | BMEGEGTBX01 | |||
| Type | study unit with contact hours | |||
| Course types and number of hours (weekly / semester) | course type: | lecture (theory) | exercise | laboratory excercise |
| number of hours (weekly): | 2 | 0 | 0 | |
| nature (connected / stand-alone): | - | - | - | |
| Type of assessments (quality evaluation) | mid-term grade | |||
| ECTS | 3 | |||
| Subject coordinator | name: | Váncza József | ||
| post: | associate professor | |||
| contact: | vancza.jozsef@gpk.bme.hu | |||
| Host organization | Department of Manufacturing Science and Engineering | |||
| https://manuf.bme.hu/ | ||||
| Course homepage | https://manuf.bme.hu/?page_id=1668 | |||
| Course language | hungarian | |||
| Primary curriculum type | mandatory elective | |||
| Direct prerequisites | Strong prerequisite | none | ||
| Weak prerequisite | ||||
| Parallel prerequisite | ||||
| Milestone prerequisite | at least obtained 0 ECTS | |||
| Excluding condition | none | |||
Aim
The aim of the course is to give a modern overview of the typical methods of artificial intelligence (AI) and their application possibilities. Students will become acquainted with the basics of artificial intelligence search, knowledge representation, inference and learning methods, the most important issues of the theoretical background of AI methods and tools that can be used to assist engineering work. After completing the course, students should be able to analyze the characteristics of the tasks arising in their work in terms of the applicability of artificial intelligence methods and tools. to perform computer modeling work with certain tools.
Learning outcomes
Competences that can be acquired by completing the course
Knowledge
1. Knows the basic approaches to artificial intelligence. 2. Knows the concept and principle of operation of a rational agent. 3. Proficient in formulating search problems, familiar with blind search methods. 4. Is familiar with the basic methods of searching using heuristics. 5. Has a basic knowledge of the mechanisms of local search methods. 6. Informed in the basic issues of knowledge and representation, syntax, semantics and interpretation. 7. Has knowledge of the basics of logical inference and programming. 8. Is aware of the use of representation tools using conceptual hierarchies. 9. Is aware that a rule- and case-based representation and inference mechanism is in place. 10. Informed about the basic problems and approaches of modern machine learning.
Ability
1. Is able to formulate an engineering problem as a search problem. 2. Ability to select a suitable search method and heuristic. 3. Able to use systems that provide advanced search methods. 4. Uses a system that supports proper logical representation. 5. Uses systems that support rule-based representation and inference. 6. Interprets the essence of a computer semantic web representation. 7. Uses a machine learning method appropriate to the particular engineering task. 8. Proposes a simpler machine learning task. 9. Proposes the technical application of artificial intelligence. 10. Interprets the main trends in the international artificial intelligence literature.
Attitude
1. Is receptive to collaborating with the instructor and fellow students in expanding knowledge. 2. Expands your knowledge of the technical application of artificial intelligence through continuous acquisition of knowledge. 3. Opens to the use of modern information technology tools. 4. Strives for an accurate and error-free solution. 5. Seeks to become familiar with and routinely use the toolkit needed to solve the problem.
Independence and responsibility
1. Independently thinks through tasks and problems and solves them based on specific resources. 2. Is committed to enriching the field of artificial intelligence with new knowledge and scientific results. 3. In case of teamwork, he / she cooperates with his / her fellow students in solving the tasks. Is responsible for formulating appropriate criticism or opinions, making decisions and drawing conclusions. 5. Committed to the principles and methods of systematic thinking and problem solving.
Teaching methodology
Education takes place in the form of weekly classroom lectures. In addition to the basics, the rapporteur will present real technical applications. The current curriculum in the online version is shared with students immediately after the lectures. Students can also optionally undertake an independent task. This could be either a solution to a problem agreed with the rapporteur that requires artificial intelligence tools, or an explanation of a controversial problem in the form of an essay. Students are free to choose the software environment and toolbar. The speaker will provide students with discussion questions and background literature. In case of a successful (at least sufficient) result, a closed task can be triggered by the independent task.
Support materials
Textbook
Stuart Russell, Peter Norvig: Artificial Intelligence in a Modern Approach (Second, Revised, Extended Edition), Panem, ISBN: 9789635454112, 2005.
Stuart Russell, Peter Norvig: Artificial Intelligence: A Modern Approach, 4th ed., Pearson, ISBN-10013461099, 20207
Lecture notes
Online material
http://mialmanach.mit.bme.hu/aima/index
http://aima.cs.berkeley.edu
http://www.aaai.org/Library/Magazine/magazine-library.php
Validity of the course description
| Start of validity: | 2021. September 1. |
| End of validity: | 2025. July 15. |
General rules
During the semester, 3 indoor dissertations (mid-term examination) will be written, the exact dates and topics of which will be given by the lecturer in the first lecture. Expected time points: Weeks 5, 8, and 13. The condition for obtaining a mid-term ticket is that all three are successful (at least sufficient) in closed places, and that the student must use the replacement no more than twice. After each closed area, students will have the opportunity to make a replacement the following week. Up to two enclosures can be replaced. It is possible to replace only one in a closed place (so-called in-place replacement writing) in the week following the diligence period. Students can optionally undertake an independent task in consultation with the lecturer. The deadline for submitting the independent task is the (12th) week before the last closed place. The task is classified by the rapporteur. In case of a successful (at least sufficient) result, a closed task can be triggered by the independent task.
Assessment methods
Detailed description of mid-term assessments
| Mid-term assessment No. 1 | ||
| Type: | formative assessment, point-in-time personal act | |
| Number: | 1 | |
| Purpose, description: | A complex, written way of evaluating the competence elements of the knowledge and ability type in the form of a dissertation. The dissertation basically focuses on the application of the acquired knowledge, so it focuses on problem recognition and solution. Practical (calculation) tasks must also be solved during the performance evaluation, the part of the curriculum on which the evaluation is based is determined by the lecturer of the subject. The maximum working time is 25 minutes. | |
| Mid-term assessment No. 2 | ||
| Type: | formative assessment, point-in-time personal act | |
| Number: | 1 | |
| Purpose, description: | A complex, written way of evaluating the competence elements of the knowledge and ability type in the form of a dissertation. The dissertation basically focuses on the application of the acquired knowledge, so it focuses on problem recognition and solution. Practical (calculation) tasks must also be solved during the performance evaluation, the part of the curriculum on which the evaluation is based is determined by the lecturer of the subject. The maximum working time is 25 minutes. | |
| Mid-term assessment No. 3 | ||
| Type: | formative assessment, point-in-time personal act | |
| Number: | 1 | |
| Purpose, description: | A complex, written way of evaluating the competence elements of the knowledge and ability type in the form of a dissertation. The dissertation basically focuses on the application of the acquired knowledge, so it focuses on problem recognition and solution. Practical (calculation) tasks must also be solved during the performance evaluation, the part of the curriculum on which the evaluation is based is determined by the lecturer of the subject. The maximum working time is 25 minutes. | |
| Mid-term assessment No. 4 | ||
| Type: | summative assessment | |
| Number: | 1 | |
| Purpose, description: | The previous three dissertations were a complex, written way of evaluating the competence elements of the knowledge and ability type. Students could also optionally undertake an independent task. The summary mid-term mark is determined by averaging the results of each dissertation or by taking into account the results obtained during the replacement (s) instead of the result of the non-accepted audit. With the result of a possible independent task, the worst or unwritten indoor result can be replaced. | |
Detailed description of assessments performed during the examination period
The subject does not include assessment during the examination period.
The weight of mid-term assessments in signing or in final grading
| ID | Proportion |
|---|---|
| Mid-term assessment No. 1 | 33 % |
| Mid-term assessment No. 2 | 33 % |
| Mid-term assessment No. 3 | 34 % |
| Mid-term assessment No. 4 | 100 % |
The weight of partial exams in grade
There is no exam belongs to the subject.
Determination of the grade
| Grade | ECTS | The grade expressed in percents |
|---|---|---|
| very good (5) | Excellent [A] | above 90 % |
| very good (5) | Very Good [B] | 84 % - 90 % |
| good (4) | Good [C] | 72 % - 84 % |
| satisfactory (3) | Satisfactory [D] | 60 % - 72 % |
| sufficient (2) | Pass [E] | 48 % - 60 % |
| insufficient (1) | Fail [F] | below 48 % |
The lower limit specified for each grade already belongs to that grade.
Attendance and participation requirements
Must be present at at least 70% (rounded down) of lectures.
Special rules for improving, retaken and replacement
The special rules for improving, retaken and replacement shall be interpreted and applied in conjunction with the general rules of the CoS (TVSZ).
| Need mid-term assessment to invidually complete? | ||
| yes | ||
| The way of retaking or improving a summary assessment for the first time: | ||
| each summative assessment can be retaken or improved | ||
| Is the retaking-improving of a summary assessment allowed, and if so, than which form: | ||
| retake or grade-improving exam possible for each assesment separately | ||
| Taking into account the previous result in case of improvement, retaken-improvement: | ||
| new result overrides previous result | ||
| The way of retaking or improving a partial assessment for the first time: | ||
| partial assesment(s) in this group can be improved or repeated once up to the end of the repeat period | ||
Study work required to complete the course
| Activity | hours / semester |
|---|---|
| participation in contact classes | 28 |
| preparation for summary assessments | 16 |
| additional time required to complete the subject | 46 |
| altogether | 90 |
Validity of subject requirements
| Start of validity: | 2021. September 1. |
| End of validity: | 2025. July 15. |
Primary course
The primary (main) course of the subject in which it is advertised and to which the competencies are related:
Mechanical engineering
Link to the purpose and (special) compensations of the Regulation KKK
This course aims to improve the following competencies defined in the Regulation KKK:
Knowledge
- Student has the knowledge of the theories and contexts of fundamental importance in the field of engineering and of the terminology which underpins them.
- Student has the detailed knowledge and understanding of the methods of knowledge acquisition, data collection, ethical constraints and problem-solving techniques in the technical field.
- Student has the knowledge of information and communication technologies in the field of engineering.
Ability
- Student has the ability to apply the general and specific mathematical, scientific and social principles, rules, relationships and procedures acquired in solving problems in the field of engineering.
- Student has the ability to apply the theories and related terminology in an innovative way when solving problems in a given field of engineering.
- Student has the ability to approach and solve specific problems within student's field of specialisation in a multi-disciplinary and interdisciplinary manner.
Attitude
- Student is open and receptive to learning, embracing and authentically communicating professional, technological development and innovation in engineering.
- Student seeks to contribute to the development of new methods and tools in the field of engineering. A deepened sense of vocation.
- Student strives to carry out their work in a complex approach based on a systems and process-oriented thinking.
Independence and responsibility
- Student has the ability to work independently on engineering tasks.
- Student takes initiative in solving technical problems.
- Student makes professional decisions independently in student's area of activity.
Prerequisites for completing the course
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Knowledge type competencies
(a set of prior knowledge, the existence of which is not obligatory, but greatly facilitates the successful completion of the subject) |
none |
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Ability type competencies
(a set of prior abilities and skills, the existence of which is not obligatory, but greatly contributes to the successful completion of the subject) |
none |