AI is no longer "the technology of the future," but the infrastructure of the present

The Artificial Intelligence program aims to train AI specialists who will be able to develop the most effective solutions for specific tasks, meaning they will be able to:

• select an appropriate model for a specific situation (e.g., prediction, classification, recommendation, anomaly detection),
• implement and tune the model,
• validate its effectiveness on real-world data and adjust the hyperparameters,
• and refine the resulting solution until it is ready for deployment in standard software systems.

Another important aspect is the "engineering" side: the program explicitly emphasizes working with non-functional requirements (speed, deployment schedule, compatibility, etc.), because in practice these often matter more than the model’s accuracy alone.

Modern AI (especially deep learning) can seem like a “black box”—the model works, but it’s not clear why. UUN emphasizes helping students understand the internal structures of models and how they function. The goal of this effort is to develop skills

• effectively select a solution and its parameters,
• debug errors in proposed solutions,
• interpret failures,
• or justify a solution in regulated or sensitive domains.

Understanding models can be difficult. That is why, at UUN, we strive to explain models from the very first day of class using simple “games” (scenarios) that allow students to grasp the models without complex mathematical tools, thereby gradually gaining the ability to understand the core of modern AI methods.

It is a well-known fact that an AI system is only as good as its data and analysis. Therefore, every AI expert should aim to develop models methodically rather than intuitively. For this reason, the curriculum also includes lessons on data analysis. These courses aim to ensure that students can draw conclusions from data, recognize that results were not generated by mistake, and rely on the obtained method parameters in future runs.

This data analysis, however, is always closely linked to AI methods, and its goal is to understand how the proposed systems work.

• How to create functional and fast algorithms – in these courses, students learn how to represent data so that computations run smoothly, and how to perform the computations themselves so that the results are (sufficiently) accurate and the computation does not take too long.
• How to choose the best solution —an integral part of modern AI is finding the best solution (even image recognition is essentially just a best-match problem). Therefore, the program includes courses that explain how basic optimization problems work and what building blocks are needed to solve them.

AI systems are not just about defining requirements and building solutions (a process in which AI itself can assist). To successfully implement them, it is also necessary to manage the development process from the initial requirements definition through to deployment. This is another key skill that students will acquire in this program. Students will learn where to develop and test programs, as well as how to deploy them, including various cloud and virtualization solutions.

An AI professional must be able to not only calculate results but also explain them: to the client, the team, and sometimes even to regulators. Throughout the program, students will frequently be required to present the solutions they have developed as part of a team to a group of evaluators during workshops. The program also includes courses that directly teach these skills, and students will have the opportunity to fully apply them while writing and discussing their bachelor’s thesis.

UUN designs the program so that, over the course of 3.5 years, students go through the "entire lifecycle of an AI solution": problem → data → model → implementation → testing → deployment → operation.

Even in the first year, foundational computer science concepts are integrated with introductory AI concepts:

• Basic concepts of artificial intelligence, providing a broad overview of the field (including language models and the evolution from chatbots to ChatGPT). Everything is explained in a simple and engaging way, without requiring in-depth knowledge of programming or mathematical fundamentals.
• Programming Fundamentals, Advanced Programming in Python (OOP, data handling, workshop)—gradually acquiring skills in a modern AI language that is further utilized in subsequent courses
• Algorithms and Data Structures (searching, sorting, complexity) as a course introducing the first steps in creating an effective algorithm and explaining why certain methods from the world of AI work.

The subsequent part of the program shifts to a series of specialized AI courses, gradually deepening students’ knowledge of AI:

• Introduction to Artificial Intelligence (2nd year) – providing an overview of AI methods and describing their applications using real-world data examples, thereby building students’ ability to understand the field and identify specific tasks suitable for AI-based solutions.
• Principles of Machine Learning (3rd year) – providing a comprehensive view of methods, including an understanding of their potential weaknesses and advantages from the perspective of application to data analysis, thereby building the ability to select the most effective method,
• Neural Networks and Deep Learning (3rd year) – providing an overview of the latest trends in AI and related methods, as well as options for training and applying them to data.

And because modern AI involves not only training a model but also its operation, the program includes a course focused on MLOps:

• Artificial intelligence development and deployment processes (CI/CD for models, monitoring, version control, reproducibility, etc.) – teaching students how to develop, train, and deploy models on modern cloud systems.
• Databases—teaching the ability to work effectively with large and complex datasets in modern information systems, which form the foundation for AI components.

Courses covering methodological foundations:

• Discrete Mathematics and Mathematical Analysis – courses focused on explaining the components necessary for developing modern AI algorithms.
• Advanced Algorithms – a course explaining how to write functional, fast, and efficient programs.

The proposed topics include areas that are currently of great interest in both industry and research, such as:

• explainable AI,
• adversarial machine learning (model attacks and defense),
• applications of LLMs (large language models) in recommender systems or search,
• “LLMs on devices with limited computational capacity,”“attacks on LLMs,”
• AI analysis of legal documents,
• multi-agent systems and simulations of information propagation in networks.

• Builds and fine-tunes models (classification, prediction, recommendation).
• Develops end-to-end pipelines from data to model, often in Python.
• Collaborates with software engineering on product integration.

• Tests hypotheses, designs experiments, and evaluates outcomes.
• Ensures: sound inference, validation, and interpretation.
• In practice, often combines analysis with model prototyping.

• It turns a "model on a laptop" into a reliable service: training automation, versioning, drift monitoring, and auditability.
• It is essential in environments where models run in production over the long term (banks, e-commerce, transportation).

• They build text-processing systems: search, document classification, virtual assistants, and chatbots.
• They address output quality, security risks, and often operational constraints (latency, costs).

• It does not address AI "in general," but rather within specific domains: e.g., predictive maintenance, fraud detection, delivery optimization, and content personalization.
• The program explicitly identifies these areas as typical applications.

The OECD shows that AI will also affect professions that do not in themselves require “specialized AI skills”—the mix of required competencies (analytical, process-oriented, and information-handling skills) is changing. This increases the value of people who understand data, models, and their implications. Graduates will thus possess excellent competencies even if they eventually change their career path.

Studying AI today makes sense not only because of the topic’s “popularity,” but mainly because AI is becoming a standard component of products and decision-making processes. The Artificial Intelligence program at Unicorn University is designed to guide students through the entire process of creating AI solutions—from algorithmic and mathematical foundations, through machine learning and neural networks, to deployment and operation. The curriculum is heavily focused on practical tasks and culminates in a mandatory internship, which significantly enhances graduates’ competitiveness.

If you want to not only use AI but truly understand it and be able to deploy it safely and effectively in real-world systems, this is the type of education that will become increasingly valuable in the coming years.