(in Polish) Sztuczna inteligencja w finansach 2600-IADdz1SIF

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.