Introducing students to the fundamentals of computing systems, which is a prerequisite for studying computer architecture and organization. Elaboration of the basic concepts of computer systems, the principle of their operation, and the implementation details. Emphasizing the difference between computer architecture and organization and mastering their fundamental aspects. Pointing out the impact of artificial intelligence and data science on the computer systems themselves. Types of application software and difference between system and application software. An application software that utilizes artificial intelligence in the backend.

The main objective of this course is to introduce students to the basic principles and concepts of programming through the Python programming language. Students will acquire theoretical and practical knowledge and understanding of the fundamentals of programming using Python. By the end of the course, students will also become familiar with the basics of object-oriented programming.

Acquisition of theoretical and practical knowledge about the principles, technologies, and tools used in the management of Big Data. Training for efficient modeling, implementation, and maintenance of databases and big data processing systems. Through this course, students will understand the concepts of databases and their role in the organization and management of data, and learn to design, implement, and maintain databases considering scalability, performance, availability, and protection. Furthermore, to use tools and technology for processing big data, such as NoSQL databases, Hadoop, Spark, etc.

Introduction to the fundamentals of discrete mathematics and algorithms necessary for the field of artificial intelligence; acquiring contemporary mathematical knowledge and skills for the areas of artificial intelligence and machine learning.

Mastery of the basics of linear algebra necessary in the field of machine learning, as well as differential and integral calculus. The goal of the course is to introduce students to basic mathematical ideas and tools that use artificial intelligence methods, algorithms and techniques. This course introduces students to the basics of matrix theory that are necessary for the analysis of linear systems. Special emphasis is given to matrix operations applied in machine and deep learning. The examination of the functions of one or more variables studied in this course is useful for modeling and analyzing physical phenomena that involve continuous changes in variables or parameters and have applications in all areas of artificial intelligence.

The aim of this course is for students to master the basic concepts of data science and understand the role of artificial intelligence within the broader discipline of data science, as one of the fundamental tools applied in this field. The course will cover the fundamentals of a wide range of methods used in data science and artificial intelligence.

Understanding the concept of exploratory (research) data analysis (EDA) as a significant phase in the development of solutions (products) in the field of data science and artificial intelligence. Understanding the principles and techniques of EDA. Developing data visualization skills. Mastering data attribute engineering techniques. Applying statistical methods for data analysis. Acquiring practical experience with data analysis tools. Understanding the importance of data preprocessing for machine learning. Applying EDA and data engineering techniques to real-world datasets.

The main objective of the course is to familiarize students with the object-oriented paradigm in the Java programming language. The secondary goal is to gain experience in implementing object-oriented programs in Java through practical work and to build a solid foundation for further study in later years. Another objective of this course is for students to acquire the necessary skills for conducting research activities in this dynamic field by leveraging existing Java environments and tools such as the Eclipse and IntelliJ software packages. The Java programming language has been chosen as the technology for learning object-oriented programming principles because, in addition to Python and R, Java also supports machine learning through specialized libraries. Furthermore, Java is widely taught as the primary programming language for object-oriented programming principles at many universities worldwide.

Understanding the role and principles of operating systems as an interface for using hardware resources in standalone and distributed computer systems. Understanding the implementation of the mentioned principles in the form of the GNU/Linux platform as the current standard for various applications of artificial intelligence. Mastery of using the command-line user interface for working on local and remote computer systems.

The goal of the course is to familiarize students with the methods and techniques that algorithms and artificial intelligence methods use for representing relevant information specific to a given task and making intelligent decisions, whether they are satisfactory (suboptimal) or optimal, all with the aim of achieving defined goals. Among other things, the course syllabus also covers some of the challenges of artificial intelligence systems, such as efficient knowledge representation, generating appropriate sequences of actions, and searching for alternatives to find optimal or near-optimal solutions.

Enhance knowledge in the field of exploratory data analysis (EDA) by utilizing advanced tools and techniques. Introduce the concept of data mining and the basic principles of these algorithms, as well as the relationship between data mining and machine learning algorithms. Discover relevant, interesting, and unusual associations and correlations among data. Familiarize with fundamental statistical models for classification and regression, data clustering algorithms, and methods for dimensionality reduction. Understand and interpret the results. Apply data mining to real-world problems. Comprehend ethical and legal aspects of data mining. By the end of the course, students will be able to efficiently explore and analyze data and draw meaningful conclusions that can be utilized for making better decisions in various domains.

The primary goal of this course is to provide students with a strong foundation in probability theory, enabling them to effectively reason under uncertainty in the context of artificial intelligence. Students will learn to analyze and model random variables and probability distributions, gaining the skills necessary for probabilistic reasoning and decision-making in AI applications. By exploring various probabilistic models and statistical inference techniques using programming language R, students will develop the ability to apply probability theory to real-world AI problems, fostering critical thinking and practical proficiency.

Introducing students to the concept of optimization, mathematical modeling, and the possibilities of implementing stohastics algorithms for solving problems defined by these models through theoretical and practical simulations. Equipping students with the ability to evaluate the quality of results for given problems. Testing on standard benchmarks and real-world problems. Comparative analysis and structural decomposition of problems and approaches to solving them.

The aim of the course is to familiarize students with the basic concepts, principles, and techniques used in most machine learning algorithms. The focus of this course is on "classical" machine learning models, without delving into the details of deep learning methods. Students will be introduced to different types of machine learning: supervised, unsupervised, and reinforcement learning. They will become familiar with various machine learning algorithms for solving regression, classification, and clustering problems. Practical work and the use of machine learning systems will be demonstrated in various application areas, such as medicine, finance, engineering, and business. It is important to note that while this course covers the conceptual foundations of machine learning, it does not emphasize mathematical proofs.

One of the main objectives of the course is to enable students to successfully apply deep learning methods, primarily artificial neural networks, to various tasks and understand their applications and challenges in different domains. Another significant goal of the course is to provide students with an understanding of the mathematical background of deep learning methods, particularly backpropagation and optimization algorithms such as stochastic gradient descent (SGD). Although the focus of the course is on classical neural networks, specifically multilayer perceptrons, students will also be introduced to newer architectures such as convolutional and recurrent neural networks, autoencoders, and transformers, as well as their applications.

Acquisition of theoretical and practical knowledge and comprehensive understanding of the concepts of cloud computing and software development, as well as their application in the context of machine learning models. Students familiarize themselves with various types of cloud technologies (infrastructure, platforms, and software services). Through this course, students will be able to identify the advantages and challenges of using cloud computing in software development. The course analyzes different service models in the cloud (IaaS, PaaS, SaaS) and implementation models (Public Cloud, Private Cloud, Hybrid Cloud), as well as key components of cloud architecture (Virtualization, VM, Storage, Networks) for the purposes of developing and deploying machine learning models. Students will become familiar with the phases of machine learning software development in cloud systems such as Microsoft Azure, AWS, and Google Cloud.

Acquisition of theoretical knowledge and practical skills for automated natural language processing, including natural language understanding, dialog management and natural language generation. Practical mastery of natural language processing algorithms. Training students to develop natural language processing tools.

The aim of this course is to provide students with an understanding of the fundamental concepts, techniques, and algorithms used in the field of digital image processing and pattern recognition. It covers different methods and algorithms for image processing and pattern recognition, as well as their applications in various domains. Students will be introduced to image processing techniques, including filtering, and their application for noise removal, contrast enhancement, and object isolation. They will gain an understanding of feature extraction techniques from images and their application in pattern recognition tasks. The course also covers image classification using classical machine learning approaches and well-known convolutional neural network models.

The goal of this course is to enable students to develop and implement their own convolutional neural networks (CNNs), which are deep learning models that have proven to achieve better performance than other deep models in working with digital images, for solving a wide range of practical challenges in the domain of computer vision. Students will also become familiar with the architecture, operations, and mathematical background of CNN models. Another objective of this course is to introduce students to generative adversarial networks (GANs) for working with digital images and their applications. The syllabus of this course is structured in a way that builds upon the subject of Image Processing and Pattern Recognition, where students had the opportunity to use well-known CNN models.

Understanding the basic theoretical concepts and principles of feature and model selection. Understanding the importance of feature selection. Understanding different techniques for feature selection, primarily filter and wrapper-based methods. Applying stochastic algorithms with wrapper-based methods for feature selection and identifying relevant features that have the highest impact on the target variable of a specific problem. Investigating different metrics (indicators) for evaluating model performance and selecting appropriate metrics for a specific application. Understanding the importance of selecting an appropriate model tailored to a given task. Gaining experience in selecting appropriate models for classification and regression challenges in real-world environments through simulationbased experiments.

The aim of the course is to master advanced techniques for modeling and forecasting time series using classical machine learning models and deep learning, primarily recurrent neural networks. It familiarizes students with the concept of time series forecasting, the adaptation of data series into time series format, and the implementation of machine learning and deep learning models to solve problems of this type. Through theoretical and practical instruction, students are equipped to implement algorithms for solving real-world problems that can be formulated as time series. The course requires knowledge of Python programming for data science and artificial intelligence, as well as basic principles of machine learning and neural networks.

Acquisition of theoretical knowledge and practical skills in the application of deep learning in natural language processing, including natural language understanding, dialog management and natural language generation. Practical mastery of deep learning algorithms in natural language processing. Training students for independent application of deep learning in natural language processing.

The course introduces fundamental topics in robotics such as C-space and motion limitations. It explains the forward and inverse kinematics and discusses velocity, force and torque calculations in robotics. Course also deals with motion planning algorithm in robotics. Both traditional and modern path planning algorithms will be discussed. Theoretical discussions will be followed by numerical and simulation exercises. Path planning algorithms will be demonstrated on simple Arduino robots.

Acquiring theoretical and practical knowledge about approaches and methods for explaining machine learning models. Enabling students to extract data-driven knowledge using various artificial intelligence methods.

The main goal of professional practice is to acquire professional experience and practical knowledge in the field of artificial intelligence and relevant technologies. Professional practice will enable students to: acquire new and additional skills and knowledge that are in demand in the job market in the field of artificial intelligence, which belongs to the students' area of interest, adapt to the work environment, connect theoretical knowledge with practical experience, orient themselves towards future employment, apply the latest artificial intelligence technologies in the industry, as well as define and specify the topic for their final thesis.

Acquiring theoretical and practical knowledge and comprehensive understanding of machine learning operations. Students will gain an understanding of the fundamentals of machine learning operation models. Identification of key elements and challenges in the process of operationalizing machine learning models. Understanding the role of operationalization in the practical application of machine learning models. Familiarizing students with software engineering, model engineering, and the latest practices in implementation with practical experience using platforms and tools. Upon completion of this course, a student will understand the lifecycle of machine learning products and everything required to translate an idea into an operational environment in an enterprise setting.

The graduation thesis represents a unique project that is carried out during the final (fourth) year of study. The graduation thesis consists of theoretical research and practical work (conducted within the course - graduation thesis course) in a specific domain of artificial intelligence and computer science. It is presented and defended as a whole. The primary goal of the graduation thesis is to familiarize the student with the rules, procedures, and processes of independent and comprehensive research work, as well as the practical applications of the acquired theoretical concepts and knowledge in algorithms and techniques of artificial intelligence. The project, presented in written form and orally, is realized within the course - graduation thesis - course.

The aim of the subject is to enable students to independently develop a project in the field of artificial intelligence of medium complexity and apply all the knowledge acquired during their studies covered by the syllabus of completed and passed courses. It involves connecting and consolidating all the acquired knowledge in the form of a practical project in a real-world environment.