Artificial intelligence and generative models

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Management

Course type:

Compulsory course

When:

4 year, 2 module

Instructor

Ternikov, Andrei

Course Syllabus

Abstract

Generative AI models user neural networks to identify the patterns and structures with existing data to generate new and original content. Examples of foundation models include GPT-3 and Stable Diffusion, which allows users to leverage the power of language. During this course students will delve into the world of AI generative models, exploring their definition, purpose, applications, and the key concepts that drive their success.

Learning Objectives

• Discover key concepts driving the success of AI technologies.
• Gain insights into generative AI models creating original content.

Expected Learning Outcomes

• OPK-3-Man. Demonstrates the capacity to develop sound organizational and managerial decisions that consider their social implications, facilitate their implementation in a complex and dynamic environment, and assess their consequences.
• PK-1-Man. Demonstrates the capacity to oversee organizations engaged in operations within the context of globalized international markets.
• PK-4-Man. Formulate conceptual models of business conduct within the context of global market dynamics and propose strategies for the effective management of risk in the pursuit of international economic activities.
• OPK-3-Econ. Demonstrates the ability to analyze and provide a meaningful explanation of the nature of economic processes at both the micro and macro levels.
• PK-3-Econ. Demonstrates proficiency in the utilization of contemporary technical resources and information technologies to address analytical and research objectives.
• OPK-5-Man. Demonstrates proficiency in the utilization of contemporary information technologies and software tools for the resolution of professional tasks, including the management of extensive data sets and their subsequent intellectual analysis.
• PK-1-Man(Log). Demonstrates an aptitude for identifying the data essential for solving set logistics tasks, for collecting and processing data, for analyzing the results of calculations, and for justifying conclusions in a manner consistent with the specific task at hand.
• PK-5-Man(Log). Demonstrates the capacity for the critical analysis of statistical and financial data, with a particular focus on the evaluation of logistics processes and the development of market forecasts based on quantitative methods.
• OPK-2-Econ. Capable of gathering, organizing, and statistically examining the data essential to solving the specified economic challenges.
• OPK-5-Econ. Demonstrates proficiency in the utilization of contemporary information technologies and software tools for the resolution of professional tasks.
• PK-4-EconMan. Demonstrates proficiency in the areas of argumentation and data-driven decision making, exhibiting the capacity to articulate and justify management decisions with clarity and persuasiveness.
• PK-10-EconMan. Demonstrates a comprehensive understanding of the tools utilized in the field, including the ability to innovatively apply both novel and established technologies in industrial and research contexts.

Course Contents

• Introduction to key concepts in generative AI
• Tools and applications of generative AI

Assessment Elements

• Tests
  Eight tests from the Online course organized via SmartLMS system (one test per each week). Each test of equal weight and of 10-points maximum. The final grade for all Tests is an average grade. Grade is not rounded.
• Seminars
  Seminars activities involving elements of ranking, teamwork, and game-based activities.
• Final Test
  Final test from the Online course organized via SmartLMS system. 10-points maximum. Grade is not rounded.

Interim Assessment

• 2024/2025 2nd module
  0.7 * Final Test + 0.1 * Seminars + 0.2 * Tests

Bibliography

Recommended Core Bibliography

• Osondu, O. (2021). A First Course in Artificial Intelligence. Bentham Science Publishers Ltd.

Recommended Additional Bibliography

• Jason Bell. (2020). Machine Learning : Hands-On for Developers and Technical Professionals: Vol. Second edition. Wiley.

Authors

• TERNIKOV Andrei ALEKSANDROVICH
• BUDKO VIKTORIYA ALEKSANDROVNA