Artificial Intelligence and Big Data Analysis 2600-MRCdz2SIf

Students will become familiar with AI technologies, in particular machine learning (ML), and their practical applications in the analysis of large datasets and human–computer interaction. The aim of the course is to develop skills in the use of ML tools to support decision-making. Participants will learn to identify both the opportunities and the limitations of implementing ML-based solutions in the context of data analysis.

The topics covered will include the following areas:

1. Data sources and data analysis environments.

2. Characteristics of data processed using machine learning algorithms.

3. Data evaluation and visualization.

4. Data preparation, standardization, and dataset splitting.

5. Selected machine learning algorithms and examples of their applications:

- Support Vector Machines (SVM)

- Classification Trees

- Random Forests (RF)

- K-means clustering

- Neural Networks

6. The prediction process.

7. Advantages and limitations of the discussed algorithms.

8. Examples of applications of the discussed algorithms in classification and regression tasks.

The discussion-based classes will be conducted using basic programming skills in the R environment and external packages appropriate for the machine learning algorithms covered in the course.

Type of course

elective courses

Learning outcomes

K_W01 The student can design and critically evaluate the implementation process of Artificial Intelligence algorithms in financial institutions, integrating advanced quantitative methodology (Economics and Finance) with corporate governance requirements (Management) and restrictive regulatory frameworks concerning model explainability and data protection (Legal Sciences).
K_W05 The student is able to perform a multi-faceted analysis of the impact of megatrends (technological, ecological, and socio-political) on the architecture of AI information systems, assessing their significance for the financial stability of organizations and identifying fundamental ethical and legal dilemmas of digital civilization in automated decision-making processes.
K_W06 The student identifies and applies the principles of industrial property protection and copyright law in the process of creating and operating AI systems, resolving dilemmas regarding the legal status of training data, the patentability of algorithmic solutions, and the rights to works generated by Artificial Intelligence in the financial sector.
K_U03 The student can independently and collaboratively prepare analyses, diagnoses, and reports concerning complex and atypical problems related to digital finance in organizations, present them communicatively to diverse audiences, and lead debates—also in English—using advanced information and communication tools (ICT).
K_U06 The student is able to develop advanced analyses and reports on atypical digital finance problems, both independently and in a team, and then communicatively present them (also in English) to various audiences and conduct debates using modern ICT tools while considering the legal, ethical, and civilizational context.
K_K05 The student can independently and collaboratively develop advanced reports on complex digital finance problems and communicatively present them (also in English) using ICT tools, demonstrating full responsibility for observing and developing professional ethical standards and fostering the heritage of the financial profession in the age of algorithmization.

Assessment criteria

Midterm exam (e.g., an online test on the e-learning platform) and participation.

Practical placement

Bibliography

1. Przewodnik po pakiecie R, Przemysław Biecek, OFICYNA WYDAWNICZA GiS, 2017
2. Sztuczna inteligencja w finansach. Yves Hilpisch, Helion, 2022
3. Praktyczne uczenie maszynowe, Marcin Szeliga, PWN, 2019
4. Uczenie maszynowe w języku R, Brett Lantz, Helion, 2024
5. Język R w data science, Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund, Helion, 2024.

Term 2026Z:

Notes

Term 2026Z:

The course requires a computer laboratory with access to the R environment and RStudio.