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Singidunum University
Applied Artificial Intelligence

The Applied Artificial Intelligence study program aims at equipping students with key theoretical knowledge and enabling them to develop practical skills in various areas of artificial intelligence, including robotics, machine learning, and explainable artificial intelligence. At the beginning of the program, through general and specialized courses in computer science, students acquire the basic knowledge and develop skills needed for work in all areas of information technology that represent the milestone to the development, operation, and understanding of artificial intelligence. The study program allows students to gain practical experience in the design, development, and integration of artificial intelligence in various types of multimedia and security systems, and to work in areas following cutting-edge trends in IT (such as quantum-enhanced machine learning and artificial intelligence, and multi-agent and distributed artificial intelligence systems). Students have the opportunity to learn from professors who are top experts in the field, among whom are five professors who are on the prestigious Stanford University list and belong to the 2% of the best scientists in the world; learning from such experts, students are provided with the opportunity to become competitive in the labor market.

Upon completing undergraduate academic studies, students will have gained knowledge in the field of modern algorithms of artificial intelligence and machine learning; they will have acquired the necessary practical and technical skills required for the design, development, and integration of artificial intelligence in practical environments, and will have been able to work in other areas of information technology. Additionally, students will have gained hands-on experience working with several programming languages and will have also gained experience developing an application and working on distributed and cloud computing systems securely. Furthermore, students will have also gained theoretical knowledge and practical experience in applying basic machine learning models, neural networks, advanced deep learning models, recurrent neural networks, generative adversarial networks, quantum computing, multimodal machine learning, and explainable artificial intelligence techniques. The study program allows students to gain practical experience in the application of these concepts in various fields, such as economics, business, finance, ecology, computer security, robotics, management, transportation, media, and others. Apart from earning the diploma, students can take additional exams and obtain certificates from global IT providers, thus increasing their competitiveness in the labor market.

The program has been created in compliance with the guidelines of the leading organization in the domain of computer education, ACM (Association of Computing Machinery), and elements of all 11 main areas of knowledge defined in the document Computing Competencies for Undergraduate Data Science Curricula. The compatibility of the Applied Artificial Intelligence study program with the stated global standards enables the mobility of students in two directions (horizontally and vertically), both during and upon completing the studies, as well as the recognition of the diploma worldwide. The program is also fully compliant with the Law on Higher Education.

Artificial intelligence is an area where great advances have taken place in recent years. Many companies are interested in incorporating new technologies from the field of artificial intelligence and integrating them into existing products or using them to develop new solutions.

Graduates have excellent employment opportunities in various IT fields and can participate in research dealing with cutting-edge topics in the field of artificial intelligence.

Graduates can work in the following positions:

  • Artificial Intelligence Developer,
  • Machine Learning Developer,
  • Machine Learning Operations Developer,
  • Computer Vision Developer,
  • Robotics Developer,
  • Natural Language Processing Developer,
  • Artificial Intelligence Trainer,
  • Big Data Developer,
  • Data Scientist,
  • Data Analyst,
  • Software Developer.

Graduates are competent to work in the following companies:

  • Google,
  • Open AI,
  • Facebook,
  • Yandex,
  • Microsoft,
  • Intel,
  • Nvidia,
  • and many other companies that want to integrate and improve their business and decision-making processes using artificial intelligence, machine learning, and deep learning solutions.

hat Tuition fee for this study program in English is 3900 € for 60 ECTS, paid in RSD.

Pre-admission Form
for Serbian Citizens

Pre-admission Form
for Foreign Citizens

I year

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.

II year

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.

III year

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.

Acquisition of theoretical knowledge and practical skills in using user interface technologies for useful and usable interaction with machine and deep learning models of artificial intelligence. Among other things, the course syllabus covers some of the challenges of user interaction with machine and deep learning models associated with improving the comprehensibility and performance of artificial intelligence systems.

The course introduces digital marketing and recommendation systems, with a focus on the use of artificial intelligence and machine learning techniques. The course covers topics such as market segmentation, market share and customer targeting, and explores how artificial intelligence can be used to improve these processes. The aim of the course is to understand the principles of segmentation and targeting in marketing and how they can be improved using artificial intelligence.

Introducing students to the basics of management, managerial history, contemporary trends in management and business, and management process (planning, organizing, leading, and controlling). Through readings, discussions and simulations, students will develop practical skills of critical thinking, analytical problem-solving, teamwork and collaboration.

The goal of the course is to familiarize students with basic ethical concepts necessary for building better technology, as well as to consider its impact on the economy, civil society, and government. It involves discussing ethical issues that arise in the development of data science and its practical applications, such as designing better ethical data governance regimes, and devising strategies to promote fairness and reduce algorithmic bias. It also involves raising awareness of and addressing ethical questions that arise from the use of artificial intelligence systems in managing our work, political, and social lives, such as the impact of automation on economic inequality, threats to workplace privacy rights, concentration of power by artificial intelligence, acceptable conditions for its use, and possible ways to incorporate human moral and other values into artificial intelligence systems.

IV year

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.

Explore the application of artificial intelligence in the field of cybersecurity. Identify the benefits and challenges of using artificial intelligence in this domain. Understand how artificial intelligence can contribute to the efficiency and security of cyber defense.

The course goals include: Understanding the principles and techniques of developing artificial intelligence agents using the LangChain library; Gaining practical skills in implementing AI agents and applying various AI algorithms, including search algorithms, knowledge representation and reasoning, planning, machine learning, and natural language processing; Developing the ability to analyze and solve complex problems using AI agent technologies, including designing and implementing intelligent decision-making agents, multi-agent systems, and conversational agents; Enhancing critical thinking and problem-solving abilities by working on hands-on projects and practical assignments using the LangChain library (and/or other AI Agent software libraries).

The goal of the course is to familiarize students with the practical implications of the "no free lunch" theorem and to understand that there is no universal model of machine learning and deep learning that will provide satisfactory results for every type of dataset. In line with this, the aim of the course is also to introduce students to the problem of hyperparameter optimization in both classical and deep learning models, primarily through the application of soft computing stochastic optimization methods, such as nature-inspired metaheuristics (evolutionary algorithms and swarm intelligence algorithms). Another objective of the course is to explore the use of regularization techniques to adjust machine learning models and prevent overfitting and underfitting issues specific to a given dataset and problem.

Introducing students to concepts, algorithms, and architectures in multimodal machine learning that enable the integration of information from different data modalities. Developing students' ability to design, implement, and evaluate multimodal machine learning systems to solve complex tasks. In addition, students will understand the advantages and limitations of multimodal machine learning and learn how to choose appropriate techniques and algorithms for specific applications. Acquiring skills necessary for teamwork and presentation of projects in this domain.

This course explores the development of innovation, entrepreneurship in the field of AI/robotics, and prepares students to develop and manage successful businesses in this field. The course covers key principles of innovation management, such as ideation, validation, scaling, prototyping, MVP, as well as the unique challenges and opportunities of entrepreneurship in the artificial intelligence and robotics industry. Through case studies, guest lectures and hands-on projects, students will develop critical thinking, problem-solving and leadership skills and gain practical experience in developing startups by preparing business plans and pitches to investors.

Acquaintance of students with the basic terms and institutes of law with the aim of comprehensive overview and full legal regulation of cyberspace with regard to intellectual property rights, data protection and privacy, the rights of robots as electronic persons, legal regulation of artificial intelligence (AI), as well as the responsibility of robots and intelligent agents.

Acquisition of theoretical knowledge and practical skills for the application of machine and deep learning algorithms in the development of video games. Hands-on mastery of video game development technology that uses artificial intelligence models (AIM). Training students to create intelligent video games. Special focus is given to the application of deep reinforcement learning algorithms (DRL) in the development of video games, primarily for creating intelligent agents.

The course goals include: a) development of a deep understanding of multi-agent systems (MAS) and decentralized artificial intelligence (AI) principles, architectures, and algorithms including consensus algorithms, game-theoretic concepts and distributed optimization; b) gaining the ability to critically evaluate the performance and scalability of MAS and decentralized AI approaches, including compromises between centralization and decentralization; b) gaining practical skills in designing, implementing, and evaluating MAS and decentralized AI solutions using Python programming language; c) exploring and analyzing real-world applications of MAS and decentralized AI in various domains such as swarm robotics, smart grids, traffic management, and finance.

* Elective courses depend on the number of candidates