| Course Title | Artificial Intelligence |
| Course Code | M-400-01 |
| Level | Master |
| ECTS Credits | 6.00 |
| Faculty/unit | Engineering Faculty |
| Field | Computer and information sciences |
| Course Type | Standard |
| Course Leader | Kijonoka Jeļena - Dr. sc. ing. assistant professor |
| Annotation | The course builds on a relational understanding of Artificial Intelligence (AI) and focuses on opportunities, limitations, and challenges related to organizational use of AI for value creation. The course introduces different types of AI technologies, machine learning principles and approaches, and how they have emerged. Through theories of how the relationship and dynamics between organizations and technology can be understood, the course highlights how AI triggers new organizational and societal challenges. Against this backdrop, the course explores how, on operational and strategic levels, businesses can work with understanding, managing and creating value using AI. |
| Aim | To give comprehensive understanding of contemporary AI technologies, the main principles of ML techniques, advantages, risks and potential disadvantages of AI deployment in business, the essence of the main stages of AI project implementation, technologies and best practices to mitigate the risks of AI project implementation. |
| LO1 | Describe different types of AI technologies and account for their evolution |
| LO2 | Account for and explain the role of AI in organizational value creation |
| LO3 | Understand machine learning principles and capable select proper approach for the business needs |
| LO4 | Account for and motivate different ethical challenges and issues raised by the use of AI in businesses |
| LO5 | Analyze organizational challenges related to the management of AI in businesses |
| LO6 | Develop recommendations for the use of AI in businesses on both operational and strategic levels |
| LO7 | Through analytical generalization, identify conditions for the use of AI in businesses |
| Required Literature | - Steven Finlay. (2021). Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies Kindle Edition. Relativistic
- 4th edition (248 pp)
- Mark Coeckelbergh. (2020). AI Ethics (The MIT Press Essential Knowledge series). The MIT Press (248 pp)
- Dr. Dr. Lance Eliot (2022). Latest Trends In AI Ethics: Practical Advances In Artificial Intelligence And Machine Learning. LBE Press Publishing. (370 pp)
- Silja Voeneky, Philipp Kellmeyer , Oliver Mueller, Wolfram Burgard. (2022). The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives (Cambridge Law Handbooks). Cambridge University Press
- New edition (500 pp).
- Kindle Edition. (2021). The AI Project Handbook: How to manage a successful artificial intelligence project. Rodeo Press
- 1st edition (202 pp)
- Iafrate, Fernando. Artificial Intelligence and Big Data : The Birth of a New Intelligence, John Wiley & Sons, Incorporated, 2018. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=5301758.
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| Assessment Method | To assess the learning outcomes of this course, several types of activities are provided, which include 1) performing automated tests independently (formative assessment) 2) performing practical / laboratory work (summary assessment) 3) studying additional materials. 4) examination (summative assessment).
Practical / laboratory work is carried out by students independently / in a group. The main task is the acquisition of practical skills and the application of theoretical knowledge gained during the classes. Based on the results of the implementation, a report is prepared, which is evaluated by the teacher using the rubrics of assessment / grading scale. In addition to the assessment, the student receives feedback on the work done. Rating and review are published in e.tsi.lv and are available to students.
Automated tests are used as a formative type of knowledge assessment and are designed for continuous self-assessment of the knowledge acquired by the student. This will allow students to pay attention to material that they have not mastered enough.
The course ends with an exam, which is aimed at assessing the theoretical knowledge and practical skills acquired by the student in the process of studying the course. |
| Independent study | Independent work is organized through the performance of several types of tasks during the study of the course. 1) Performing automated tests after each topic of the course (published in e.tsi.lv). To complete them, students must review the materials presented in the classroom. 2) Preparation of reports on the implementation of practical / laboratory tasks. 3) Study of additional materials published in e.tsi.lv. Also, within the allocated time for self-study, the student prepares for the final exam. |
| Full-time |
|---|
| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
|---|
| Reports | 50 | | LO1, LO2, LO3, LO4 | | Presentations | 25 | | LO3, LO5, LO6, LO7 | | Examination | 25 | | LO6, LO7 |
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