Compulsory courses (for all AI students)

Methods in AI research (compulsory)

Because of its interdisciplinary character, the variety of techniques used in artificial intelligence is considerable. In this course an overview is provided in three modules which focus on different aspects of artificial intelligence: techniques from logic and linguistics (module 1), from computer science (module 2) and from cognitive neuroscience (module 3). Reasoning being the general theme, the course shows what forms this can take in the different areas, ranging from idealization (module 1) to computation (module 2) to experiments (module 3).In the first module the students will study the formal aspects of reasoning in artificial intelligence. After an introduction describing the emergence, through the ages, of formal reasoning in philosophy and the sciences, students will be introduced to various formal systems and methods of proof, such as natural deduction, sequent calculi, Hilbert-style systems and display calculi. It will be shown how different views on reasoning can be captured in these proof systems. As an example, intuitionistic logic, linear logic as used in linguistics and fuzzy logic will be discussed. The type theoretic Curry-Howard isomorphism that connects proofs to programs will be treated. It will be shown how one can in a precise way capture various aspects of reasoning, such as its complexity, by the structure and size of proofs. The relation to famous open problems in computer science is explained. At the end of this module students will be able to construct proofs in the various proof systems and to translate proofs from one format to another. They understand and can use the mentioned logics and understand the different views on reasoning underlying them. They understand the proofs-as-programs paradigm and know what a normal or cut-free proof is.The second module covers various foundational techniques that are used for the development of intelligent systems in general and artificial agents in particular. We begin by refreshing the memory of the student by giving a crash course into modal logic. We begin with the general framework (syntax and Kripke-style semantics) and review applications such as epistemic logic, doxastic logic, temporal logic, dynamic logic and deontic logic. Also so-called minimal model modal logic with neighbourhood semantics with as application coalition logic. Students will do exercises with these standard techniques to be properly prepared for the other courses in the curriculum, in particular Intelligent Agents. Following the modal logic part, we focus on multi-agent programming techniques that can be used to implement multi-agent systems. We present a multi-agent programming language, present its operational semantics, and explain how its properties can be analysed by means of modal logic. The students will work with some programming exercise to master the use of the programming language. The final part of this module discusses probabilistic techniques for multi-agent learning, such as conditional expectation, Markov chains, Markov reward chains, decision reward chains, Markov decision processes (MDPs), stochastic games (multiple-player MDP's).In the third module, students will be introduced to current methods in cognitive brain research with examples taken from vision and language research using classroom lectures and practicals. This module will cover the entire spectrum of skills and techniques available to the cognitive neuroscience community, including neurophysiological research methods (such as fMRI and EEG), psychophysics, experimental design, modeling and basics data analyses. The practicals will give students hands-on experience with a number of techniques to create experiments in vision and/or language, acquire data and analyze the results from these experiments using SPSS and Matlab.

Philosophy of A.I. (compulsory)

This course will make students familiar with fundamental issues in the philosophy of AI, and will introduce them to several current discussions in the field. Students will practice their argumentation and presentation skills, both in class discussions and in writing.
The course is split up in three parts. The first part is a quick overview of the fundamental issues and core notions in philosophy of AI. It addresses topics such as the Turing Test, the Chinese Room Argument, machine intelligence, machine consciousness, weak and strong AI, and the Symbol System Hypothesis. In order to establish a shared background for all students, the material of this part will be assessed with an entrance test already in week 3.
In the second part of the course, there will be an in-depth discussion of several current topics in the field, for example on ethics and responsibility in AI, transhumanism, or the relation between AI and data science. On each topic, there will be a lecture, and a seminar with class discussions and student presentations. Students prepare for those discussions by posting a thesis with one or more supporting arguments about the required reading. In the third part of the course, students will write a philosophical paper, and will provide feedback on their fellow students' draft papers.

This course is for Students Artificial Intelligence, History and Philosophy of Science, and RMA Philosophy. Students of other MA-programmes, please contact the Course Coordinator.
The entrance requirements for Exchange Students will be checked by International Office and the Programme coördinator. Therefore, you do not have to contact the Programme coördinator yourself.

Primary electives

Logic and Language

This course covers advancedmethods and ideas in the logical analysis of language, especially in relation to type-logical grammars, the parsing-as-deduction paradigm, and their combination with formal semantics of natural language. The course has a 'capita selecta' format, focusing on various aspects of the connection between language and reasoning. The 2014-2015 installment studies discourse dynamics from the perspective of continuations and continuation-passing-style interpretations. In the first part of the course, we study the origin of these concepts computer science (the control operators from programming language theory) and logic (double negation embeddings of classical logic into intuitionistic logic). In the second part of the course, we discuss the growing body of literature on natural language semantics that uses continuations to explicitly include the context of evaluation as a parameter in the meaning composition process. Topics include quantifier scope and evaluation order, cross-sentential anaphora, dynamic logic with exceptions.

Students Artificial Intelligence, for registration please contact your programme coordinator during the enrollment period.

Intelligent agents

This course is about the theory and realisation of intelligent agents, pieces of software that display some degree of autonomy, realised by incorporating "high-level cognitive / mental attitudes" into both modelling and implementation of this kind of software. The agent concept calls for an integration of several topics in artificial intelligence, such as knowledge representation and reasoning (in particular reasoning about action and change) and planning. In the course time is devoted to the philosophical and theoretical (mostly logical) foundations of the area of intelligent agents. Furthermore, ways of realising them by special architectures and so-called agent-oriented programming languages in which one can program the "mental states" of agents are described. This course presents the introductory theory for the agent-oriented courses in the Master programme.body { font-size: 9pt;

Cognitive Modeling

Formal models of human behavior and cognition that are implemented as computer simulations - cognitive models - play a crucial role in science and industry.

In science, cognitive models formalize psychological theories. This formalization allows one to predict human behavior in novel settings and to tease apart the parameters that are essential for intelligent behavior. Cognitive models are used to study many domains, including learning, decision making, language use, multitasking, and perception and action. The models take many forms including dynamic equation models, neural networks, symbolic models, and Bayesian networks.

In industry, cognitive models predict human behavior in intelligent 'user models'. These user models are used for example for human-like game opponents and intelligent tutoring systems that adaptively change the difficulty of a game or training program to a model of the human's capacities. Similarly, user models are used in the design and evaluation of interfaces: what mistakes are humans likely to make in a task, what information might they overlook on an interface, and what are the best points to interrupt a user (e.g., with an e-mail alert) such that this interruption does not overload them?

To be able to develop, implement, and evaluate cognitive models and user models, you first need to know which techniques and methods are available and what are appropriate (scientific or practical) questions to test with a model. Moreover, you need practical experience in implementing (components of) such models.

In this course you will get an overview of various modeling techniques that are used world-wide and also by researchers in Utrecht (esp. in the department of psychology and the department of linguistics). You will learn their characteristics, strengths and weaknesses, and their theoretical and practical importance. Moreover, you will practice with implementing (components of) such models during lab sessions.

Relationship between goals and examination
The learning goals will be examined in three ways:

  1. Students will implement components of cognitive models in computer simulations during computer practicals. These assignments will be graded.
  2. Students will evaluate the scientific literature by orally presenting and critiquing scientific papers that include cognitive models. The presentation and critiquing will be graded.
  3. Students will be tested on their general knowledge of cognitive models in an exam.

Multi-agent systems

This course focuses on multi-agent issues and will consist of lecture, seminar and lab sessions.
The lectures will cover the following topics:

  • Game theory
  • Auctions
  • Communication
  • Social choice
  • Mechanism Design
  • Normative Multi-Agent Systems

The seminar sessions consists of student presentations and will cover other multi-agent system issues such as:

  • Logics for Multi-Agent Systems
  • Multi-Agent Organisations and Electronic Institutions
  • Normative Multi-Agent Systems
  • Argumentation and Dialogues in Multi-Agent Systems
  • Multi-Agent Negotiation
  • Communication and coordination in Multi-Agent Systems
  • Development of Multi-Agent Systems

Each student is expected to present some papers on one of the abovementioned topics.
In the lab sessions the students will develop multi-agent systems on different platforms such as 2APL and Jade.

Experimentation in Psychology and Linguistics

Both science and industry are interested in creating precise formal models of human behaviour and cognition. To help build, test and optimise such models, one needs to create and run experiments. Students participating in this course will learn (I) how to design experiments given an existing model, (II) how to implement experiments using various tools and, finally, (III) how to extract data from the recorded responses for analysis purposes.

Most theoretical claims in linguistics and psychology are made by positing a formal model. The aim of such models is to make precise predictions. Moreover, the predictions of a model need to tested with formal experiments. The results of the experiment may or may not lead to changes in the model and thus lead to a new set of testable predictions. Essential in the modelling-experimenting cycle is careful experimental design. The course covers the practical and theoretical considerations for experimental research, from posing the research question to interpreting and reporting experimental results.

In industry, experiments are also used frequently. For example, to assess how people use interfaces (e.g., where do they look or click, or how particular text influences their subsequent choices?), to test what the best design of a product is, or to test the appropriateness of a user model (e.g., do people learn what the model predicts them to learn, do they have a more immersive experience when a model guides adaptation of the software?).

In this course you will get an overview of various experimenting techniques that are used world-wide and also by researchers in Utrecht (esp. in the department of psychology and the department of linguistics). You will learn how to use such techniques for testing specific models, as well as where the limits of these technique lie. In the practicals you will also gain hands-on experience with the implementation, data manipulation and data analysis steps of experimenting.

The learning goals will be examined in three ways:

  1. Students will read and critically reflect on selected articles from the experimental literature. They will prepare a short presentation based on the critical reflection. The presentation will be graded.
  2. Students will implement experiments and work with experimental data during practicals. These will be graded.
  3. Students will design and implement an experiment on a topic of their own choice and write a note reporting on the experiment. Implementation and report will be graded.

Commonsense reasoning and argumentation

In commonsense reasoning, people are often faced with incomplete, uncertain or even inconsistent information. To deal with this, they use reasoning patterns where it can be rational to accept a conclusion even if its truth is not guaranteed by the available information. This course focuses on logics that systematise rationality criteria for such `defeasible' reasoning patterns. Logics of this kind are often called `nonmonotonic logics', since new information may invalidate previously drawn conclusions. This course covers some of the best-known nonmonotonic logics, in particular default logic, circumscription and argumentation systems, as well as formal theories of abductive reasoning. Attention is paid to the use of these formalisms in the specification of dynamic systems and in models of multi-agent interaction.body { font-size: 9pt;

Logic and Computation

Students will learn how to answer one or more of the following research questions by means of an actor-based methodology in which each question will be addressed from multiple perspectives.+ What is a program?+ What is a computer?+ What are the practical implications of undecidability?+ What is the distinction between a stored-program computer and a universal Turing machine?+ What is the difference between a model (of computation) and a physical computer? This is a reading &writing course. Attendance is obligatory. Homework will already be handed out during the first week of class with a firm deadline in the second week. Late arrivals in class will only be tolerated once; in other cases, they can lead to a deduction of the student’s final grade. The aim of the course on proofs as programs is to get an understanding of type theory and its role within logic, linguistics, and computer science and get acquainted to the Curry-Howard correspondence, relating types to propositions and programs to proofs.

This course is for students in Artificical Intelligence, as well as students in History and Philosophy of Science and the RMA Philosophy. Students of other MA-programmes, please contact the Course Coordinator. Students History and Philosophy of Science and Artificial Intelligence experiencing problems with enrollment, please contact the Student Desk Humanities,

Multi-agent learning

Multi-agent learning (MAL) studies software agents that learn and adapt to the behaviour of other software agents, that themselves adapt to the behaviour of other software agents, and so on. The presence of other learning agents complicates learning, which makes the environment non-stationary (moving target) and non-Markovian (not only experiences from the immediate past but also earlier experiences are relevant). With adaptive agents it also becomes less beneficent to only adapt to the behaviour of other agents, on the pain of being exploited by more steadfast agents that do not follow but instead impose their strategy on others. Important topics of adaptive agents include statistical learning and single-agent reinforcement learning. Important topics of MAL include (evolutionary) game theory, fictitious play, gradient dynamics, no-regret learning, multi-agent reinforcement learning (MinMax-Q, Nash-Q), leader (teacher) vs. follower (learner) adaptation, and the emergence of social conventions. Examples of domains that need robust MAL algorithms include manufacturing systems (managers of a factory coordinate to maximise their profit), distributed sensor networks (multiple sensors collaborate to perform a large-scale sensing task under strict power constraints), robo-soccer, disaster rescue (robots must safely find victims as fast as possible after an earthquake) and recreational games of imperfect information such as poker. Indeed, poker and simplified forms of poker are an important topics of research in multi-agent learning.

Advanced Topics in Cognitive Science

Machine learning with deep convolutional neural networks (deep learning) is being applied increasingly broadly in computer science, technology and scientific research. This method allows computer systems to perform tasks that have previously been impossible or inaccurate for computers, but typically straightforward for humans. Tasks like visual object identification and natural language processing have traditionally been investigated by cognitive scientists and linguists, but recent applications of deep learning to these tasks also positions them at the center of recent artificial intelligence developments. Therefore, it is important for AI students and researchers to understand the links between cognitive science and AI.

In this course, you will learn the principles behind deep learning, an approach inspired by the structure of the brain. You will learn how these principles are implemented in the brain, focusing on the two aspects of visual processing and language (semantic or syntactic) processing. You will build your own deep learning systems for the interpretation of natural images and language, using modern high-level neural network APIs that make implementation of these systems accessible and efficient.

The course goals will be examined in the following ways:
- Students will attend lectures introducing the approach taken in deep learning systems, comparing this to how deep learning is implemented in biological brains, and introducing the main applications of deep learning to cognitive science and linguistics. Their understanding of this content will be assessed in a final exam.
- Students will participate in discussions and reviews of relevant literature, which will be graded.
- Students will work through lab practical assignments on visual processing and on language processing. The resulting reports will be graded .

Secondary electives

Research internship AI

A research internship is a project that should be performed by a student and under the guidance of a supervisor. The topic of the research internship should directly be relevant for artificial intelligence and agreed by the supervisor. The projects can involve the development of a software, a theoretical investigation, or an experimental research (see below for some project examples). The projects can be performed either internally in our department, or externally by other departments of our university, other universities or companies. The project should always be performed under the guidance of, and in agreement with, an internal supervisor. The students can have their own concrete project ideas or they may be interested in doing a project on a specific topic. In both cases, they can contact a supervisor with the expertise in the topic of the project in order to discuss the details of the project and if and how it can be performed as a research internship. In some cases, and in agreement with the supervisor, two students can perform a project together.Project examples:
- Agent-based Traffic Simulation
- A Power-based Spectrum for Group Responsibility
- Implementation of an agent library in Unitybody { font-size: 9pt;

Modelling and system development

Dit vak ("MSO") behandelt de kunst van object georienteerd analyse en ontwerp. Er is ook aandacht voor de verschillende zaken die, naast het daadwerkelijke programmeren, aan bod komen tijdens het bouwen van software, zoals requirements engineering, testen, refactoren, en software development processen.MSO gaat verder waar Imperatief Programmeren ophoudt. Bij MSO leer je meer over het analyseren van de problemen die klanten aandragen en het ontwerpen van geschikte oplossingen.

Neurocognition of Memory and Attention

Period (from – till): March 2019 - June 2019
Prof. Dr. J.L. Kenemans, Sociale Wetenschappen / Bètawetenschappen – Psychologische Functieleer,
Prof. Dr. A. Postma, Sociale Wetenschappen – Psychologische Functieleer,
Prof. Dr. J.J. Bolhuis, Sociale Wetenschappen / Bètawetenschappen – Psychologische Functieleer,
Prof. N. Ramsey, UMCU.
Course descriptionTopics in Memory and Attention research, especially those concerning the interface of attention and memory (e.g., working memory and the control of selective attention), as well as the interfaces between memory/ attention and other domains (perception, action, emotion). The main emphasis is on underlying neurobiological processes, as revealed in human and animal models.

Literature/study material used:

L. Kenemans & N. Ramsey (2013. Psychology in the brain: Integrative cognitive neuroscience (293 pages). Palgrave Macmillan.

Articles: To be announced

Apply via the study guide and at least 1 week before start of the course. The maximum of participants is 40.

Mandatory for students in own Master’s programme:

Optional for students in other GSLS Master’s programme:

Prerequisite knowledge:
Relevant bachelor, basic neuroscience (as in “Cognitive Neuroscience” by Gazzaniga et al.)

Social and Affective Neuroscience

Period (from – till): January 2019 - March 2019
Dr. Estrella Montoya
Departement Psychologie
Faculteit Sociale Wetenschappen
4 colleges, 100% van het voorbereiding en nakijk werk voor de examens

Dr. Peter Bos
Departement Psychologie
Faculteit Sociale Wetenschappen
2 colleges

Dr. Jack van Honk
Departement Psychologie
Faculteit Sociale Wetenschappen
2 colleges

Dr. David Terburg
Departement Psychologie
Faculteit Sociale Wetenschappen
1 college
Course description
This course offers comprehensive knowledge of the theoretical and experimental paradigms in the neuroscience of social and emotional behavior, based on the latest developments in these fields. The future of science as a “unity of knowledge” best reflects itself in Social and Affective Neuroscience. The primary aim is to teach students about the state-of-the-art in these multidisciplinary burgeoning fields, which combine neuroscience, psychology, biology, endocrinology, and economics, and to show how this multidisciplinary approach contributes to new knowledge concerning brain functions and social psychopathologies (e.g. social phobia, psychopathy, autism).
In this course we want to show you how the exciting field of social neuroscience looks like today, not only by giving an overview of the most important work in this field but also by letting you practice with the activities of a social neuroscientist. Therefore, this course offers both theoretical lectures and practical sessions. Each Social & Affective Neuroscience course day starts with a lecture and is followed by an activity or assignment in which you become a social neuroscientist yourself.

Literature/study material used
Recent Scientific Review Articles on the Neuroscience of Emotion and Emotional Disorders (updated each year).
Via the study guide.
Mandatory for students in Master’s programme
* CN students are strongly recommended to follow one of these courses:
Social and Affective Neuroscience and/or Neurocognition of memory and attention

Optional for students in other GSLS Master’s programme:

Prerequisite knowledge:
Relevant BA

Philosophy of Neuroscience

Period (from - till): June 2019

Course description
This course is offers compact, rigorous and practical journey in the philosophy of neuroscience, the interdisciplinary study of neuroscience, philosophy, cognition and mind. Philosophy of neuroscience explores the relevance of neuroscientific studies in the fields of cognition, emotion, consciousness and philosophy of mind, by applying the conceptual rigor and methods of philosophy of science. The teaching will start with the basics of philosophy of science including the work of Popper, Lakatos, Kuhn and Feyerabend, and use a methodological evaluation scheme developed from this work that allows rigorous evaluating neuroscientificresearch as science or pseudoscience. Furthermore, there will be attention for the historical routes of neuroscience starting with Aristotle, and the conceptual problems in neuroscience, methodological confusions in neuroscience, dualism and fysicalism. The main aim of the course is provide wide-ranging understanding of the significance, strengths and weaknesses of fields of neuroscience, which helps in critical thinking, creativity, methodological precision and scientific writing.

Literature/study material used
Book Chapters and Articles on Neurophilosophy and Philosophy of Neuro(science).
Via the study guide.

Mandatory for students in own Master’s programme:

Optional for students in other GSLS Master’s programme:

Semantics and Pragmatics: Variation and Representation

The semantics/pragmatics distinction is an integral part of the received wisdom in formal linguistics. Nevertheless, the nature of the distinction is very much subject to discussion itself and, in fact, many of the hot topics in the study of meaning today straddle the semantics/pragmatics divide in interesting and largely unexpected ways. The goal of this course is to allow the student to understand the controversies that exist as well as the theoretical frameworks that accommodate such issues. The successful student will be able to conduct research within the context of such frameworks herself.

The semantics/pragmatics divide is often paralleled to Grice’s distinction between saying and implicating. More broadly, however, one could say that while semantics is responsible for conventional aspects of meaning, such as truth-conditions and conditions of use, pragmatics models conversational aspects of meaning: roughly, the way a particular use may modify semantic meaning. The very division between semantics and pragmatics has never been uncontroversial, however. Interestingly, the emerging debates rely heavily on empirical methods that are new to the field, ranging from experimental to computational methods.

Apart from introducing the general semantics-pragmatics distinction, each year the course focuses on one particular topic within the combined field of semantics and pragmatics. Example topics are: scalar implicature, presupposition, expressives, normativity and modality, subjectivity, etc.

Speech production & perception

This course looks at current research into the production and perception of speech. We will pay specific attention to the psycholinguistic and neurocognitive processes that underlie speech production and perception, and the interaction between the two. In addition, we will discuss the impact of (various types of) impairments on the production and perception of speech in adults and on speech (and language) development in children; the relation between underlying deficits, compensatory adaptations and how these express themselves in symptomatology. A recurring theme in the course is the interplay of clinical and theoretical issues. How can theory-driven research support clinical work, and how do clinical questions affect (fundamental) work on speech production and perception?

Comparative Psycholinguistics

The course focuses on the comparative analyses of children, brain damaged patients and healthy individuals with respect to their linguistic capacities. In addition, we discuss the so-called special registers, that is to say expressions produced by healthy individuals which bear resemblance to the speech of individuals with aphasia or typically developing children. Students are also introduced to the main tenets of information theory and its application to language research as the theoretical basis for the proposed comparative approach.

Empirical Approaches to Formal Semantics

Semantic theory relies on various empirical methods, including experimental psycholinguistics, corpus analysis and linguistic questionnaires. The course presents topics where developing formal semantic theories heavily depends on empirical work: conceptual semantics, common sense reasoning, plurals and generics. Students choose a theoretical problem and study selected articles on that problem. Based on this study, students formulate an empirical hypothesis and test it in the end project.     

Career orientation:
Experimental research.

Language, Communication and Emotion

When you read or listen to a bit of language, you are somehow assigning meaning to an unfolding sequence of signs. Because of the representational and computational complexity involved, this process of interpretation is considered to be one of the major feats of human cognition. However, you also happen to be just another mammal, and as such you are biologically predisposed to have 'affective' or 'emotional' responses to the environment, that is, to feel certain things about agents, objects and events around you (including, above all, other people). In this course, we explore how these two acts of assigning meaning relate to one another, by connecting psycholinguistic thinking about language processing and representation with evolutionary views on Homo Sapiens as an intrinsically affective and ultra-social species. In the first part of the course, you will become acquainted with modern theories on emotion and related affective phenomena (moods, preferences), as well as with an analytical framework for thinking about the interfaces between emotion and language comprehension. In the second part, we’ll explore a number of concrete research topics where language and emotion are deeply intertwined, including verbal insults and swearwords, morally loaded language use, and the role of language in emotion regulation. During the second part, you will also be able to work on a topic of your choice. For example, affective semantics, sarcasm and affective prosody, indirectness and politeness, emoticons, gossiping and complaining. Note: Students who have participated in "De Gevoelige Communicator" should get in touch with the course coordinator before signing on to the current course.

Career orientation:

Because of the highly interdisciplinary nature of the topic (spanning linguistics, psycho/neurolinguistics, emotion science and research on sociality in various fields) you will experience what it is like to do interdisciplinary research

Topics in Epistemology and Philosophy of Science

This “Topics Seminar” explores in depth issues and texts in the area of epistemology and philosophy of science, including issues related to explanation, reliabilism, scepticism, justification, the status of thought experiments or scientific authority. The specific topic will be different each time, so as to tailor it to current research developments in the field.

Previous topic (2016-17): “Philosophy of Probability and Statistical Inference”:
It’s well known that probabilistic and statistical methods play an important role in the natural and social sciences. It’s perhaps less well known (at least among non-specialists) that these methods are also an important part of the philosopher’s toolbox: probabilistic and statistical methods have found fruitful applications in logic, epistemology, the philosophy of science, ethics, social philosophy, the philosophy of religion, and elsewhere.

In this course, you’ll learn about the philosophical interpretations and applications of probabilistic and statistical methods. At the end of the course, you’ll be familiar with the central topics in the philosophy of probability theory and statistics to the extent that you can find your own way around the contemporary literature.

The specific topic and instructor(s) for the coming year will be announced in the spring.
This course is for students in the RMA Philosophy programme and History & Philosophy of Science; students from other M.A. programmes (such as Applied Ethics), should check with the course coordinator or the RMA Philosophy coordinator (, before enrolling, to ensure that they have the requisite philosophical background. The entrance requirements for Exchange Students will be checked by International Office and the Programme coördinator. Therefore, you do not have to contact the Programme coördinator yourself.

Program semantics and verification

There is no content available for this course.

Technologies for learning

In this course you will study advanced software technologies for learning, such as serious games in which you have to develop a sustainable city, simulations such as a virtual company that you have to run, competing against several other virtual companies, intelligent tutoring systems for learning mathematics, physics, or logic, etc. In particular, you will study the underlying intelligence necessary to determine what a student has learned, what a student should do next, give feedback to a student, etc.In this course you will learn about the use of software technology to support student learning.
Student learning is supported by applications such as:

  • Serious games
  • Simulations
  • Intelligent Tutoring Systems
  • Exercise Environments
  • Automatic Assessment Systems

These applications use technologies such as:

  • Model tracing: does a student follow a desirable path towards a solution?
  • Static and (sometimes) dynamic analysis: what is the quality of a student solution?
  • Learning analytics: what do students do in a learning application?
  • User modeling: what does a student know?

which build upon:

  • Strategies, parsing and rewriting
  • Bayesian networks
  • Datamining
  • Constraint solving
  • Artificial Intelligence
  • Domain-specific technologies, such as compiler technology for the domain of programming.

Probabilistic reasoning

Human experts have to make judgments and decisions based on uncertain, and often even conflicting, information. To support these complex decisions, knowledge-based systems should be able to cope with this type of information. Probability theory is one of the oldest theories dealing with the concept of uncertainty. In this course, probabilistic models for manipulating uncertain information in knowledge-based systems are considered. More specifically, the theory underlying the framework of probabilistic networks are considered, and the construction of such networks for real-life applications are discussed.body { font-size: 9pt;

Data mining

If properly processed and analyzed, data can be a valuable source of knowledge. Data mining provides the theory, techniques and tools to extract knowledge from data. Learning models from data can also be an important part of building a decision support system. In turn, the computer plays an increasingly important role in data analysis: through the use of computers, computationally expensive data mining methods can be applied that were not even considered in the early days of statistical data analysis. In this course a number of well-known data mining algorithms are coved. The type of problems they are suited for, their computational complexity and how to interpret and apply the models constructed with them are covered.body { font-size: 9pt;

Evolutionary computing

Evolutionary algorithms are population-based, stochastic search algorithms based on the mechanisms of natural evolution. This course covers how to design representations and variation operators for specific problems. Furthermore convergence behavior and population sizing are analysed. The course focuses on the combination of evolutionary algorithms with local search heuristics to solve combinatorial optimization problems like graph bipartitioning, graph coloring, and bin packing.body { font-size: 9pt;

Big data

There is no content available for this course.

Pattern set mining

Pattern mining is characteristic for data mining. Whereas data analysis is usually concerned with models – i.e., succinct descriptions of all data – pattern mining is about local phenomena. Patterns describe – or even are – subgroups of the data that for some reason are deemed interesting; a description and a reason that usually involves some – if any -- of the variables (attributes features) rather than all. In the past few decades – the total existence of data mining – pattern mining has proven to be a fruitful research area with many thousands of papers describing a wide variety of pattern languages, interestingness functions, and even more algorithms to discover them. However, there is a problem with pattern mining. Databases tend to exhibit many, very many patterns. It is not uncommon that one discovers more patterns than one has data. Hardly an ideal situation. Hence, the rise of pattern set mining. Can we define and find relatively small, good sets of patterns? In this course we’ll start with a brief discussion of pattern mining. After that we discuss parts of the literature on pattern set mining; only parts because there is too much to discuss it all. What types of solutions have been proposed? How do they work and, actually, do the work?body { font-size: 9pt;

Multimedia retrieval

Multimedia retrieval is about the search for and delivery of multimedia documents: images, sound, video, 3D scenes, and the combination of these. This course deals with the technical aspect of multimedia retrieval such as techniques, algorithms, and data structures for search query formulation, media feature description, matching of descriptions, and indexing.body { font-size: 9pt;

Pattern recognition

In this course we study statistical pattern recognition and machine learning.

The subjects covered are:

General principles of data analysis: overfitting, the bias-variance trade-off, model selection, regularization, the curse of dimensionality.
Linear statistical models for regression and classification.
Clustering and unsupervised learning.
Support vector machines.
Neural networks and deep learning.

Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.

Software architecture

The course on software architecture deals with the concepts and best practices of software architecture. The focus is on theories explaining the structure of software systems and how system’s elements are meant to interact given the imposed quality requirements.Topics of the course are:

  • Architecture influence cycles and contexts
  • Technical relations, development life cycle, business profile, and the architect’s professional practices
  • Quality attributes: availability, modifiability, performance, security, usability, testability, and interoperability
  • Architecturally significant requirements, and how to determine them
  • Architectural patterns in relation to architectural tactics
  • Architecture in the life cycle, including generate-and-test as a design philosophy; architecture conformance during implementation
  • Architecture and current technologies, such as the cloud, social networks, and mobile devices
  • Architecture competence: what this means both for individuals and organizations

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Natural language generation

The taught component of the course will consist of four parts:

I. General Introduction. In the first part of the course you will learn what the different aims of practical and theoretical NLG can be, what are the main elements of the standard NLG pipeline, how NLG systems are built, and how they are evaluated. Template-based and end-to-end systems will be discussed briefly.

II. Practical systems. You will get acquainted with a range of practical applications of NLG; a few will be discussed in detail: candidates applications are medical decision support, knowledge editing, and robo-journalism. Strengths, weaknesses, and opportunities for the practical deployment of these systems will be discussed. If time allows, we will devote attention to multimodal systems, which produce documents in which pictures or diagrams complement a generated text.

III. Module in focus: Referring Expressions Generation. We will zoom in on one part of the standard NLG pipeline, which is responsible for the generation of referring expressions (e.g., as when an NLG system says “the city where you work”, or “the area north of the river Rhine”). We will discuss a range of rule-based algorithms, and some that are based on Machine Learning.

IV. Perspectives on NLG. We will discuss what linguists, philosophers, and other theoreticians have to say about human language production, and how this relates to NLG. We may start with a Gricean approach, and continue with the Bayesian-inspired Rational Speech Acts approach. We will ask how accurate and how explanatory existing NLG algorithms are as models of human language production (i.e., human speaking and writing), and what are the main open questions for research in this area.

The core of the course will be presented in lectures. Additionally, students will be asked to read, present, and discuss some key papers and systems which illustrate the issues listed above.

Business intelligence

This course deals with a collection of computer technologies that support managerial decision making by providing information of both internal and external aspects of operations. They have had a profound impact on corporate strategy, performance, and competitiveness, and are collectively known as business intelligence.During this course the following BI topics will be covered:

  • Business perspective
  • Statistics
  • Data management
  • Data integration
  • Data warehousing
  • Data mining
  • Reporting and online analytic processing (i.e., descriptive analytics)
  • Quantitative analysis and operations research (i.e., predictive analytics)
  • Management communications (written and oral)
  • Systems analysis and design
  • Software development

Free choice options (30 EC):

  • Do a research internship (7,5 or 15 EC)
  • Choose master courses within or outside the UU (subject to approval)
  • Choose courses freely from the following UU master programmes:

Neuroscience and Cognition 
e.g. Neurocognition of memory and attention

e.g. Linguistic data analysis, Acquisition and Linguistic Theory

e.g. Philosophy of Mind, Science and Epistemology III

Computing Science
e.g. Probabilistic reasoning, Evolutionary Computing, Datamining.

Game and Media Technology
e.g. Multimedia Retrieval, Computer Animation, Path Planning, Games and Agents.

Business Informatics
e.g. Software Architecture, Business Intelligence.

Master thesis project (45 EC)

In the final thesis project the student carries out a research project under the supervision of one of the staff members of the research groups offering the AI programme. The project can be done within Utrecht University or in a research-and-development department of a company or research institute, or at a foreign university. In the past, students have carried out external thesis projects in such companies as KPN, Origin, The Dutch Tax and Customs Office, Vitatron Medical B.V, TNO, NS, ING, STRO, VSTEP, LibRT and Playlogic Game Factory, and at foreign universities or with companies in Australia, Finland, Sweden, Germany, Italy, Spain, the UK, the USA and Switzerland.

When done within the Utrecht University, your final thesis project is monitored by a supervisor from the AI-programma teaching staff. When the final project is conducted within a company or external institute, you will be guided by both a local supervisor within the company/institute and a supervisor of the AI-programme teaching staff.