VSFS:B_AUI Applied Artificial Intellig. - Course Information

B_AUI Applied Artificial Intelligence

Extent and Intensity

2/0/0. 3 credit(s). Type of Completion: z (credit).

Teacher(s)

Ing. Renata Janošcová, Ph.D. (seminar tutor)
Ing. Hana Lipovská, Ph.D. (seminar tutor)
doc. Ing. Naděžda Petrů, Ph.D. (seminar tutor)

Guaranteed by

doc. Ing. Naděžda Petrů, Ph.D.
Subdepartment of Management and Marketing – Department of Economics and Management – Departments – University of Finance and Administration
Contact Person: Ivana Plačková

Timetable of Seminar Groups

B_AUI/pAPH: Wed 14:00–14:44 E225, Wed 14:45–15:30 E225, R. Janošcová
B_AUI/pEKFMPPPH: Wed 8:45–9:29 E007KC, Wed 9:30–10:15 E007KC, H. Lipovská
B_AUI/pEKKV: each even Tuesday 12:15–12:59 KV202, each even Tuesday 13:00–13:45 KV202, each even Tuesday 14:00–14:44 KV202, each even Tuesday 14:45–15:30 KV202, except Tue 14. 10. ; and Mon 13. 10. 10:30–12:00 KV202, 12:15–13:45 KV202, N. Petrů
B_AUI/pMMO: each odd Tuesday 12:15–12:59 M14, each odd Tuesday 13:00–13:45 M14, each odd Tuesday 14:00–14:44 M14, each odd Tuesday 14:45–15:30 M14, N. Petrů
B_AUI/vEKPH: Fri 3. 10. 17:30–19:00 E227, 19:15–20:45 E227, Fri 7. 11. 17:30–19:00 E227, 19:15–20:45 E227, Fri 5. 12. 17:30–19:00 E227, 19:15–20:45 E227, N. Petrů

Prerequisites

This course has no prerequisites.

Course Enrolment Limitations

The course is offered to students of any study field.

Course objectives

The aim of the course is to provide students with a systematic understanding of the applications of artificial intelligence (AI) in business practice, management, marketing, finance, law, and security-related fields. Students will become familiar with the principles and capabilities of language models, generative AI, computer vision, speech recognition, chatbots, and virtual assistants. The course is designed to develop the ability to critically evaluate AI tools, propose their appropriate use, and reflect on the ethical, legal, and societal implications of their implementation.

Learning outcomes

Upon successful completion of the course, the student will be able to:
distinguish the main types of artificial intelligence, understand their development, structure, and current role in the technological ecosystem;
explain the principles of large language models (LLMs) and assess their applicability in real-world practice;
describe the possibilities of using AI in text generation and understanding, including the ability to recognize their limitations and benefits;
identify the principles of computer vision and demonstrate their application in various fields (e.g., security, healthcare, industry, manufacturing, logistics, occupational safety and health, marketing);
understand the potential of generative visual AI in marketing, presentation, and content creation, while being aware of its ethical risks;
describe the architecture and function of chatbots and voicebots, including the differences between various technological approaches;
explain the principles of speech-to-text and text-to-speech systems, including the possibilities of voice cloning and the use of avatars;
recognize and evaluate the potential of AI tools in management and decision-making processes;
list AI tools applicable in marketing, e-commerce, and customer support, and analyze their impact on customer experience;
explain the legal and ethical aspects associated with the use of AI, including current standards and regulations (e.g., EU AI Act, GDPR);
reflect on the social and cultural impacts of AI development, including the risks of disinformation, bias, and trustworthiness;
search for, compare, and critically evaluate specific AI tools in terms of their suitability for a particular purpose.

Syllabus

• Lectures Introduction to the course Applied Artificial Intelligence. History of AI, basic concepts, the difference between symbolic and machine learning-based AI, overview of current trends. Examples of applications in various fields, discussion of benefits and risks. Assignment of the team project.
• Generative AI I: Language Models (LLMs) – principles and tools. What an LLM is, how GPT, BERT, Claude, etc. work. Prompting, tokenization, fine-tuning, safety layers.
• Generative AI II: Applications in text practice and content creation support. Automated reports, summarization, translation, legal and academic texts. Drafting outlines, video scripts, AI as an assistant for teachers and managers.
• AI and Computer Vision I: Image and object recognition. Object detection, OCR, anomaly detection, applications in industry, medicine, security. OpenCV, YOLO, Google Vision, Azure AI Vision.
• AI and Computer Vision II: Generative visual AI and editing. Use of tools such as DALL·E, Midjourney, RunwayML, Adobe Firefly. Creation of marketing visuals, product photos, deepfakes, and moral limits.
• Chatbots and Voicebots I: How they work, when to deploy them. Difference between FAQ chatbots and conversational AI, NLP vs. scripted approaches. Practical demonstrations (Dialogflow, Rasa, MS Bot Framework, Chatlayer.ai).
• Chatbots and Voicebots II: Conversation design and integration into services. Building structured dialogues, voice interfaces, connection to CRM/API. Practicing testing and evaluation of a “good bot” – team analysis.
• Speech Generation and Recognition I: TTS, STT, Voice Cloning. Principles of text-to-speech (TTS) and speech-to-text (STT). Overview of tools: ElevenLabs, Microsoft Azure Speech, Google Speech.
• Avatars and Synthetic Media II: Combining multimedia for practical applications. AI speakers, video avatars, automated webinars, and virtual assistants. Use cases in e-learning, customer support, and business presentations.
• AI in Management and Finance. Prediction, scoring, risk analysis, models for decision support. Reporting automation, generative dashboards, support for data teams.
• AI in Marketing, E-commerce, and Customer Experience. Segmentation, sentiment analysis, personalized content, recommender systems. AI in emailing, PPC campaigns, social media management, behavioral models.
• Legal, Ethical, and Societal Impacts of AI. Current legislation (EU AI Act, copyright, data protection). Deepfakes, bias, accountability, trust in AI. Discussion: What would a “good” AI for society look like?

Literature

required literature
• HOLÍK, J., & RYCHNOVSKÝ, D. (2024). Umělá inteligence: Moderní přístupy a praktické využití. Praha: Grada Publishing.

recommended literature
• KNIHOVÁ L. AI Marketing Playbook: Jak ChatGPT a umělá inteligence mění svět marketingu, 2024. ISBN 978-80-271-5226-1.
• JANOŠCOVÁ, R. 2016. Computer aided of knowledge discovery in databases. In: International Conference on Management - Trends of Management in the Contemporary Society. - Brno: Mendel
• DŘÍMALKA F. Budoucnost nepráce. 2023. ISBN 978-80-11-03715-4
• Kolektiv autorů. Jednoduše: umělá inteligence. 2023. ISBN 9788024292939
• VALDA V. Rozhovory s umělou inteligencí. 2023. ISBN 978-80-908235-2-5
• RUSSELL, S. J., & NORVIG, P. 2021. Artificial Intelligence: A Modern Approach, 4th Edition, Global. Harlow, UK
• BERKA, P. 4IZ450 – Dobývání znalostí z databází. Praha: VŠE, 2006 - 2021. https://sorry.vse.cz/~berka/4IZ450/
• JAMSA, K. Introduction to Data Mining and Analytics. Burlington : Jones & Bartlett Learning, 2021. ISBN: 9781284180909. EBSCO (e-kniha, dostupná přes IS VŠFS)
• NAQVI, Al. Artificial Intelligence for Audit, Forensic Accounting, and Valuation : A Strategic Perspective, John Wiley & Sons, Incorporated, 2020. ProQuest Ebook Central.ISBN:9781119601883.ProQuest Ebook (e-kniha přes IS VŠFS)
• SVOBODA, M. (2023). AI ve vzdělávání: Možnosti, rizika, etické otázky. Olomouc: Univerzita Palackého.
• BUCHTELA, M. (2024). AI v marketingu a e-commerce. Praha: Professional Publishing.

Teaching methods

The course uses the following forms and teaching methods to achieve a high level of student engagement and retention.
Lectures with visualizations, model schematics, technology comparisons, and the historical development of AI.
Demonstrations of AI tools directly during lectures, interactive demos (e.g., prompt input into GPT, image generation, use of TTS/voicebot, etc.).
Live demonstrations and discussions on the application of tools, creation of an avatar, or playback of AI audio output with guided reflection.
Short case studies / practice-based scenarios showing how AI is transforming marketing, project management, and customer service.
Embedded questions during lectures (using Socrative, Mentimeter, polls).
Assignment and continuous reminders of the team project, with reflections on individual topics and their application to real-world problems (AI in HR, education, retail, etc.).

Assessment methods

Form of completion: credit (3 ECTS). Requirements for students:
**Attendance:**
in full-time study, at least 75 % attendance,
in part-time study, at least 50 % attendance.
Personal attendance is preferred; however, classes will also be streamed via Microsoft Teams, recorded, and recordings will be stored in the Information System (IS).
**Active participation:** engagement in discussions, case studies, and ongoing assignments during the semester.
**Project (individual or team, max. 3 students per group):**
The project consists of two parts:
**Practical output** – for example:
video, case simulation, interactive chatbot, application design, presentation with data analysis, text document, research article, literature review, case study, etc.
**Word document** – detailed scenario description including: project objective, applied method and AI tools, justification of the chosen topic, solution procedure, description and rationale of the proposed solution, evaluation of results including benefits and possible efficiency improvements compared to the original approach.
The document must always state the name of the student responsible for the respective part. This student will then present the results (in the last class or at a date set by the lecturer).
**Submission:** Both parts of the project (practical output + Word document) must be uploaded before the presentation into the Assignment Submission in IS: https://is.vsfs.cz/auth/el/vsfs/leto2025/B_AUI/ode/. A folder with the name/student ID (UČO) of the student or group must be created, into which both files will be uploaded.
**Evaluation:** relevance and originality of the topic (30 %), quality of AI tool application and practical output (40 %), ability to reflect and critically evaluate the results (30 %).

Language of instruction

Czech

Further comments (probably available only in Czech)

The course can also be completed outside the examination period.

Teacher's information

Study materials (lectures, video recordings, ...) of the subject can be found in IS VŠFS

• Enrolment Statistics (Winter 2025, recent)
  
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