Your guide to the AI bachelor at VU Amsterdam
Course guides, study tips, practice quizzes, and curated resources, built by students, for students.
What you'll find here
Everything you need for the AI bachelor, in one place.
Course Guides
Tips, quizzes, and resources for every course
Blog & Advice
Student life tips and study insights
FAQ
Common questions answered
Academic & Career
Program, careers, and internships
Student Life
Living, transport, housing, and more
About this project
I'm Jasper, an AI student at VU Amsterdam since 2024, and I built this site as a community project, completely open source and meant to be shared. Feel free to use everything here, but keep in mind that course content can change from year to year, so we can't guarantee everything is still 100% accurate. Always check the studiegids or your course page on Canvas for the latest info. If the content still matches, use all the quizzes and summaries as much as you'd like! And if you spot something outdated, we'd love your help updating it.
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Course guides
Tips, quizzes, and resources for every course in the program.
Year 1
Computational Thinking
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.
English Language Test
Assessment of English proficiency for the English-taught AI bachelor program.
Introduction to Artificial Intelligence
Overview of AI: concepts, history, applications, and main subfields including machine learning, knowledge representation, and intelligent systems.
Introduction to Psychology and its Methods
Foundations of psychology and research methods: cognition, perception, and empirical approaches relevant to human-centred AI.
Intelligent Systems
Core concepts of intelligent systems: knowledge representation, reasoning, search algorithms, and agent architectures.
Introduction to Python Programming for AI
Python programming fundamentals: variables, control flow, data structures, and libraries essential for AI development.
Project Intelligent Systems
Hands-on group project where you build, evaluate, and write a scientific report about intelligent game-playing bots for the card game Schnapsen.
Logic and Sets for AI
Mathematical foundations: propositional logic, predicate logic, set theory, and formal reasoning for AI.
Modelling Human Behaviour
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 (BETA)
Academic writing skills for AI students: formal English style, structuring arguments, citing sources properly, and writing a literature-review essay.
History of AI
Historical development of computing and AI: from punched cards to stochastic parrots, exploring key milestones, paradigm shifts, and the societal impact of digital culture.
Human-Computer Interaction for AI
Designing AI systems for human use: usability, interfaces, and the interaction between humans and intelligent systems.
Applied Programming for AI
Advanced programming for AI: web technologies, HTML/CSS/JavaScript, Git, RESTful APIs, and building data-driven web applications.
Information Management
Managing information systems: databases, data structures, and information organisation for AI applications.
Year 2
Knowledge and Data
Methods and technologies for expressing knowledge and data on the Web: RDF, RDFS, OWL, SPARQL, Linked Data, and Knowledge Graphs.
Data Structures and Algorithms for AI
Data structures, algorithms, complexity analysis, and trade-offs for AI applications.
Robot Interaction
Social robotics: human-robot interaction, communicative robots, application areas, psychology, NLP, ethics.
Linear Algebra and Calculus
Fundamental concepts in Linear Algebra and Calculus: vectors, matrices, linear systems, functions, limits, differentiation, eigenvalues, and diagonalization.
Multi-Agent Systems
Agents, multi-agent systems, knowledge representation, reasoning, and Prolog programming for building intelligent systems.
Project Conversational Agents
Develop a conversational agent for the cooking domain using DialogFlow, ontology, and natural conversation framework.
Project Socially Aware Computing
Agent-based simulation for societal challenges: Netlogo, modeling, experimentation, and research reporting.
Machine Learning
Core machine learning: linear models, neural networks, decision trees, ensemble methods, gradient descent, backpropagation, and deep learning.
Probability and Statistics
Probability theory, random variables, distributions, law of large numbers, central limit theorem, estimation, confidence intervals, and hypothesis testing.
Text Mining for AI
NLP, linguistics, text mining: rule-based systems, machine learning, deep learning, text classification, sentiment, entity recognition, topic modeling.
Databases
Relational databases: ER diagrams, relational model, SQL, schema design, normalization, functional dependencies, concurrency.
AI and Law
Relation between AI and Law; applications of AI within Law; ethical questions related to AI in the legal domain.
AI in Health
AI techniques applied to the medical field: diagnosis, treatment, monitoring, and prevention. Ontologies, explainable ML, guidelines.
Project Collective Intelligence
Collective intelligence, collective dynamics, simulation: microscopic vs macroscopic modeling, swarm robotics, agent-based simulation.
The Law of Artificial Intelligence
Legal, ethical, and societal implications of AI developments; EU AI Act; designing AI systems compliant with the law.
Year 3
Bachelor Project Artificial Intelligence
Individual research project concluding the AI bachelor: literature review, applied AI research, thesis, and oral presentation.
Ethical AI
Philosophical foundations and technical frameworks for responsible AI: moral agency, fairness, transparency, accountability, and the EU AI Act.
Automata and Complexity
Formal languages, automata, grammars, computability theory, and complexity classes (P, NP, NP-complete). Finite automata, pushdown automata, Turing machines.
Computational Intelligence
Optimization algorithms for AI: evolutionary algorithms, neural networks (deep learning), reinforcement learning, and neuroevolution.
Cognitive Psychology for AI
Understanding the mind and brain: perception, attention, memory, language processing, decision-making, and experimental design in cognitive science.
Research Design for AI
Scientific research skills for AI: research questions, literature reviews, methodology, data analysis, ethics, and scientific writing for the BSc thesis.