Browse all courses in the AI bachelor program. Each course has tips, practice quizzes, and curated resources.
Foundations of computational thinking: solution strategies, algorithms (search, sorting, graph), and problem-solving for AI. One of the most accessible and beginner-friendly courses in the programme.
Assessment of English proficiency for the English-taught AI bachelor program.
Overview of AI: concepts, history, applications, and main subfields including machine learning, knowledge representation, and intelligent systems.
Foundations of psychology and research methods: cognition, perception, and empirical approaches relevant to human-centred AI.
Core concepts of intelligent systems: knowledge representation, reasoning, search algorithms, and agent architectures.
Python programming fundamentals: variables, control flow, data structures, and libraries essential for AI development.
Hands-on group project where you build, evaluate, and write a scientific report about intelligent game-playing bots for the card game Schnapsen.
Mathematical foundations: propositional logic, predicate logic, set theory, and formal reasoning for AI.
Develop and apply models of human cognition and behaviour to the design of human-centred systems, exploring agent-based, cognitive, and collective behaviour approaches.
Academic writing skills for AI students: formal English style, structuring arguments, citing sources properly, and writing a literature-review essay.
Historical development of computing and AI: from punched cards to stochastic parrots, exploring key milestones, paradigm shifts, and the societal impact of digital culture.
Designing AI systems for human use: usability, interfaces, and the interaction between humans and intelligent systems.
Advanced programming for AI: web technologies, HTML/CSS/JavaScript, Git, RESTful APIs, and building data-driven web applications.
Managing information systems: databases, data structures, and information organisation for AI applications.
Methods and technologies for expressing knowledge and data on the Web: RDF, RDFS, OWL, SPARQL, Linked Data, and Knowledge Graphs.
Fundamental concepts in Linear Algebra and Calculus: vectors, matrices, linear systems, functions, limits, differentiation, eigenvalues, and diagonalization.
Agents, multi-agent systems, knowledge representation, reasoning, and Prolog programming for building intelligent systems.
Core machine learning: linear models, neural networks, decision trees, ensemble methods, gradient descent, backpropagation, and deep learning.
Probability theory, random variables, distributions, law of large numbers, central limit theorem, estimation, confidence intervals, and hypothesis testing.
NLP, linguistics, text mining: rule-based systems, machine learning, deep learning, text classification, sentiment, entity recognition, topic modeling.
Data structures, algorithms, complexity analysis, and trade-offs for AI applications.
Develop a conversational agent for the cooking domain using DialogFlow, ontology, and natural conversation framework.
Relational databases: ER diagrams, relational model, SQL, schema design, normalization, functional dependencies, concurrency.
Collective intelligence, collective dynamics, simulation: microscopic vs macroscopic modeling, swarm robotics, agent-based simulation.
Legal, ethical, and societal implications of AI developments; EU AI Act; designing AI systems compliant with the law.
Social robotics: human-robot interaction, communicative robots, application areas, psychology, NLP, ethics.
Agent-based simulation for societal challenges: Netlogo, modeling, experimentation, and research reporting.
Relation between AI and Law; applications of AI within Law; ethical questions related to AI in the legal domain.
AI techniques applied to the medical field: diagnosis, treatment, monitoring, and prevention. Ontologies, explainable ML, guidelines.
Individual research project concluding the AI bachelor: literature review, applied AI research, thesis, and oral presentation.
Philosophical foundations and technical frameworks for responsible AI: moral agency, fairness, transparency, accountability, and the EU AI Act.
Scientific research skills for AI: research questions, literature reviews, methodology, data analysis, ethics, and scientific writing for the BSc thesis.
Formal languages, automata, grammars, computability theory, and complexity classes (P, NP, NP-complete). Finite automata, pushdown automata, Turing machines.
Optimization algorithms for AI: evolutionary algorithms, neural networks (deep learning), reinforcement learning, and neuroevolution.
Understanding the mind and brain: perception, attention, memory, language processing, decision-making, and experimental design in cognitive science.
