| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | An Introductory Course in Artificial Intelligence |
| Course code | InfT3049 |
| Credit points (ECTS) | 3 |
| Total Hours in Course | 81 |
| Number of hours for lectures | 32 |
| Number of hours for seminars and practical classes | 37 |
| Independent study hours | 12 |
| Date of course confirmation | 11/01/2023 |
| Responsible Unit | Institute of Computer Systems and Data Science |
| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The aim of this course is to provide students with basic theoretical knowledge of artificial intelligence, its application in various fields (such as finance, agriculture, healthcare, etc.) and how different artificial intelligence models are built. The study course is learned independently, using learning materials posted on the e-study portal. Materials consist of theoretical materials (lectures), practical works (assignments) and tests. | |
| Learning outcomes and their assessment | |
| Knowledge:
• in the application of artificial intelligence; Skills: • justify what type of AI model would be most suitable for the development of a specific solution; • prepare data for further use in a machine learning model; • use various artificial intelligence platforms that offer you to create your own machine learning models without using programming languages Competencies: • independently evaluate whether the given problem can be solved using artificial intelligence and what digital tools should be used. |
|
| Course Content(Calendar) | |
| Topic Lecture Independently work
1. Introduction to Artificial Intelligence (AI); 2 h; 2. AI Applications ; 2 h; 3. AI and Machine Learning ; 3 h; 2 (Self-check test) h 4. Artificial Intelligence and Ethics ; 2 h; 1(Self-check test) h 5. Understanding Data ; 2 h; 1 (Self-check test) h 6. Data Analytics 101 ; 2 h; 1 (Self-check test) h 7. Supervised Learning ; 3 h; 1 (Self-check test) h 8. Regression and Machine Learning ; 3 h; 4 (Assignment) h 9. Microsoft Azure Machine Learning Studio and other tools ; 2 h; 1 (Self-check test) h 10. Practical Assignment using Azure Machine Learning Tool ; 1 h; 5 (Assignment) h 11. Practical Assignment using Azure Machine Learning Tool ; ; 5 (Assignment) h 12. Unsupervised Learning ; 3 h; 1 (Self-check test) h 13. Reinforcement Learning ; 3 h; 1 (Self-check test) h 14. Practical Assignment using Azure Machine Learning Tool ; ; 5 (Assignment) h 15. Machine Vision and its Applications ; 2 h; 16. Several practical assignments on Machine Vision, in Azure Machine Learning Studio. ; ; 9 (Assignment) h 17. Natural Language Processing and its Practical Applications ; 2 h; 18. Several practical assignments on Natural Language Processing, in Azure Machine Learning Studio. ; ; 9 (Assignment)h 19. Final Test; ; 2 h |
|
| Requirements for awarding credit points | |
| In order for the study course to be counted as successfully completed:
1) The student must submit at least 80% of all practical work - self-check tests and practical work. The self-check test is considered submitted if the student has answered at least 70% of the questions correctly. 2) Successful completion (at least 65%) of the final exam in test format. If the student has not submitted at least 80% of the practical work, he is not admitted to the final test. |
|
| Description of the organization and tasks of students’ independent work | |
| The study course is learned independently, by the student, using the materials added to the e-study portal.
The student follows the attached course plan, which describes both the order in which the lectures of the study course must be studied and the tasks to be completed after the lecture. After certain lectures, the student must complete self-check tests, which are attached to the study course and available on the e-study portal. The self-check tests are designed in such a way that the student can check his knowledge after learning the theory on the specific topic. In addition, during the study course, the student has to do practical work. Descriptions of the tasks of the practical works are attached and available on the e-study portal. The student completes the tasks online using the free version of "Microsoft Azure Machine Learning Studio". |
|
| Criteria for Evaluating Learning Outcomes | |
| The completion of a study course is evaluated with a pass/fail grade. To pass the course, the student must complete at least 80% of all practical work and successfully pass (at least 65%) the final exam in a test format. | |
| Compulsory reading | |
| Ethem Alpaydin. Introduction to Machine Learning (Fourth Edition), The MIT Press, 712 pp., 2020. ISBN: 9780262043793
Stuart Russell,Peter Norvig. Artificial Intelligence: A Modern Approach (4th Global ed.), Pearson Education, 1168pp., 2021. ISBN: 9781292401133 https://learn.microsoft.com/en-us/training/paths/machine-learning-foundations-using-data-science/ https://www.javatpoint.com/artificial-intelligence-tutorial |
|
| Periodicals and other sources | |
| https://www.coursera.org/learn/ai-for-everyone
https://www.coursera.org/learn/machine-learning https://developers.google.com/machine-learning/glossary |
|
| Notes | |
| Elective course for bachelor programmes. | |