Minor «Fundamentals of Artificial Intelligence»
The minor is designed to provide students with fundamental knowledge of artificial intelligence and modern methods of its application. During the course, students study the basic principles of building intelligent systems, methods of working with data, and foundational machine learning algorithms. Completing the program enables students to understand how AI technologies function and to apply them to solving typical practical tasks across various fields.
Aim | Formed key competencies | Learning outcomes |
Familiarizing students with the fundamentals of artificial intelligence, modern AI methods and tools, machine learning algorithms, neural networks, as well as the opportunities for applying them to data analysis and solving professional tasks across various fields. | • Applying mathematical methods for AI modeling. • Programming and Algorithm Development • Building and using machine learning and neural network models. • Data analysis and applying AI to solve professional tasks. | • Understand what artificial intelligence and machine learning are, and how they work through simple examples. • Be able to write simple programs and use them to solve tasks. • Use popular libraries (such as NumPy, Pandas, Scikit-learn) to work with data and build simple models. • Possess basic methods of data analysis and visualization. • Understand how different algorithms differ from each other and choose the appropriate one for a given task. • Apply basic machine learning models and neural networks to solve small real-world problems. • Evaluate model performance and understand how well they work. |
№ | Minor Courses | Description |
1 | Fundamental foundations of Artificial Intelligence | The course studies the fundamental principles of artificial intelligence and the key methods necessary for working with AI models: working with matrices and linear transformations, derivatives and gradients, probabilistic and statistical approaches, as well as optimization methods. It provides a foundation for understanding, building, and applying machine learning and neural network models for data analysis, forecasting, and solving practical tasks. |
2 | Tools and Methods of Data Analysis | The course is aimed at developing students’ fundamental knowledge and practical skills in data analysis applied to artificial intelligence tasks. It covers methods for data collection, cleaning, transformation, and visualization using modern tools such as Seaborn, NumPy, Pandas, Scikit-learn, and Matplotlib, as well as mastering work in Google Colab for organizing computational experiments. |
3 | Machine learning and Introduction to neural networks | The course explores the main types of learning (supervised, unsupervised, reinforcement), methods of regression, classification, and clustering, machine learning algorithms (decision trees, kNN, SVM), as well as basic neural networks and the fundamentals of deep learning. It helps students understand how AI models are built and how to apply them for data analysis, forecasting, and working with images and texts. |