How To Learn Artificial Intelligence

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Artificial Intelligence MSc

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Research in Artificial Intelligence concerns the analysis and modelling of tasks that are commonly assumed to require human intelligence, as well as the design of systems that can perform or support such tasks. Such research requires a wide varience of activities, from observing and interviewing human expert to designing and implementing computer programs, and creating mathematical models.

Artificial Intelligence integrates computer science with (cognitive) psychology. Other ingredients are biology, linguistics, philosophy and logic, all used to understand and describe the underlying principles of human cognitive processes, including reasoning and natural language understanding. For these reasons Artificial Intelligence is a broad and multi-disciplinary research area.

The programme consists of a Bachelors study (taking 3 years) and a Master study (taking 2 years). The Bachelors study is dedicated to providing the student with a broad and thorough basis in Artificial Intelligence, whereas the Masters provides the student with an opportunity to specialise in an area and further deepen his knowledge of AI in general. Both Bachelors and Masters studies are organised by the Faculty of Sciences in close cooperation with the Faculty of Psychology and Pedagogy, and the Faculties of Linguistics and Law. Furthermore, the students can follow courses at the Universiteit van Amsterdam. Information about the Bachelor programme can be found in a separate study guide.

Depending on the chosen Master programme the student attends lectures in other faculties, for example Psychology, Linguistics, Economy, Law, Social Sciences, and Biology. Graduation projects vary from practical to rather

fundamental, depending on the preferences and capacities of the students. Students can go to companies, research institutes or universities either in The Netherlands or abroad.

Examples of projects and locations, and more information on what such a project entails, can be found at:

http://www.cs.vu.nl/ai > Term Projects.

Masters in Artificial Intelligence are employed by companies that develop AI-systems either for their own company (for example banks, insurance companies) or in commission for other companies (software companies). Masters in AI are also employed as consultants, for example for the management of knowledge within organisations. Research and education is another area where masters in AI build a future for themselves, for example at universities or research institutes doing research in Artificial Intelligence.

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Inhoudsopgave

AI and Law 1

Research variant KTIIA 1

Individuele vakken 1

Recommended optional courses. 2

Minor Bioinformatica 2

Minor BIOINFORMATICA Compulsory optional courses 3

Minor BIOINFORMATICA Compulsory courses 3

Compulsory Courses 3

History, philosophy & social aspects of science 4

Research variant Cognitieve Science 4

Recommended Optional Courses 4

Masterproject 5

AI Vervallen vakken 1-9-2011 6

Research variant CISO 6

Research variant TAI 9

Compulsory Optional course (Software Engineering) 12

Research Variant Human Ambience 12

Keuzevakken 13

Optional courses Health 14

Optional courses Mental Fuctioning/Health 14

Optional courses Movement 14

Optional courses Social Functioning/Networks 15

Optional courses Criminology 15

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Vak: Advanced Information Retrieval 17

Vak: Advanced Logic 17

Vak: Advanced Network Analysis, Annual Seminar in Organizations 18

Vak: Advanced Selforganisation 19

Vak: Advanced Statistics for Experimentation 20

Vak: Aging and Dementia 20

Vak: Algorithms in Sequence Analysis 21

Vak: Automated Reasoning in AI 23

Vak: Behavioral Methods 24

Vak: Behaviour Dynamics 24

Vak: Bioinformatics for Translational Medicine 25

Vak: Biosystems Data Analysis 27

Vak: Brain Imaging 27

Vak: Cluster and Grid Computing 28

Vak: Coding and Cryptography 29

Vak: Comparative Modeling 30

Vak: Computer and Network Security 30

Vak: Computer Graphics 31

Vak: Computer Networks Practical 32

Vak: Concurrency and Multithreading 32

Vak: Coordination Dynamics: principles and clinical applications 33

Vak: Data Mining Techniques 35

Vak: Distributed Algorithms 36

Vak: Distributed Multimedia Systems 37

Vak: Distributed Systems 38

Vak: Dynamica van Lineaire Systemen 39

Vak: E-Business Innovation 40

Vak: Energy Flow Models 41

Vak: Evolutionaire genetica 42

Vak: Evolutionary Computing 43

Vak: Experimental Design and Data Analysis 44

Vak: Fundamentals of Bioinformatics 45

Vak: Fysieke veiligheid en crisisbeheersing 47

Vak: Game Theory [MC] 48

Vak: Gedrag en communicatie in organisaties deel 1 48

Vak: Health Promotion and Disease Prevention 49

Vak: Health Psychology 50

Vak: History and Philosophy of the Information Society 51

Vak: Human Ambience Innovation 51

Vak: Human Information Processing 52

Vak: Intelligent Web Applications 53

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Vak: Interpersoonlijke communicatie deel 1 54

Vak: Knowledge and Media 55

Vak: Knowledge Management and Modeling 55

Vak: Literature Study 56

Vak: Logical Verification 57

Vak: Master Project 58

Vak: Master Thesis: Research Project Cognitive Science 59

Vak: Memory and Memory Disorders 59

Vak: Mini Master Project AI 60

Vak: Misdaadanalyse 61

Vak: Model-based Intelligent Environments 62

Vak: Multimedia Authoring 63

Vak: Neural Models of Cognitive Processes 63

Vak: Neural Networks 64

Vak: Neuropsychology of Cognitive Dysfunctioning 65

Vak: Operating Systems 66

Vak: Operating Systems Practical 66

Vak: Organizational Space and Technology 67

Vak: Parallel Programming 68

Vak: Parallel Programming Practical 69

Vak: Perception 69

Vak: Perception for Action 70

Vak: Performance Analysis of Communication Networks 71

Vak: Prevention of Mental Health Problems 72

Vak: Project Agent Systems 73

Vak: Research methods 74

Vak: Review Paper 75

Vak: Ruimtelijke criminologie 76

Vak: Scientific Writing in English 77

Vak: Seminar Attention 79

Vak: Seminar Cognitive Neuroscience 79

Vak: Service Oriented Design 80

Vak: Sociale netwerken en organisaties 81

Vak: Software Architectuur 82

Vak: Software Asset Management 83

Vak: Software Testing 83

Vak: Special Topics Cognitive Science 85

Vak: Sport Biomechanics 85

Vak: Structural Bioinformatics 86

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-AI and Law

Research variant KTIIA

This programme contains elements so that the graduate student has a good overview of the contemporary literature regarding applications of

intelligent web-sites and intelligent agents on the internet.

Furthermore, the student learns techniques and methods from Artificial Intelligence that are used in internet applications. The master graduate student is a capable designer of intelligent web-sites, and applications based on intelligent agents. The student ensures that his/her designs respect the needs of the company for which the design is meant.

An increasingly number of companies starts to document knowledge in the company with the use of knowledge acquisition and knowledge modeling techniques from AI. By doing this, the conduct of business is being

simplified. Also with change processes in companies, automated knowledge intensive methods can be used, together with elements from economics and organisation psychology. For graduates, these is a multi-disciplinary

field of labour. Via company internships and company funded research projects there is good contact with industry.

Audience: students with an interest in analysing, modelling, simulating and experimenting with dynamics properties.

The progamme consists of 120 credits

- compulsory courses 69 credits (including a Master Project of 30 credits)

- optional courses 51 credits

Note: Every programme, including the choice of optional courses, has to be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board.

Master Coordinators: Dr. M. Hoogendoorn K room T-320a T +31 (0) 20 598 7772 E [email protected]

Dr. S. Schlobach (International students) K room T-365

T +31 (0) 20 598 7678 E [email protected]

Opleidingsdelen:

Individuele vakken

Recommended optional courses. Compulsory Courses

History, philosophy & social aspects of science

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-Recommended optional courses.

Opleidingsdelen:

Minor Bioinformatica

Vakken:

Minor Bioinformatica

Opleidingsdelen:

Naam Periode Credits Code

Advanced Logic Periode 4 6.0 X_405048

Advanced Selforganisation Period 2 6.0 X_400434

Automated Reasoning in AI Period 5 6.0 X_400389

Computer and Network Security

Period 5 6.0 X_400127

Concurrency and Multithreading

Periode 5 6.0 X_405064

Data Mining Techniques Period 5 6.0 X_400108

Distributed Algorithms Period 4 6.0 X_400211

Distributed Multimedia Systems

Period 4 3.0 X_405003

Distributed Systems Period 2 6.0 X_400130

E-Business Innovation Periode 1 6.0 X_405051

Experimental Design and Data Analysis

Periode 5 6.0 X_405078

Game Theory [MC] Periode 2 5.0 X_418021

Internet programming Periode 1 6.0 X_405082

Knowledge and Media Periode 2 6.0 X_405065

Logical Verification Ac. Year (September) 6.0 X_400115

Mini Master Project AI Ac. Year (September) 6.0 X_400428

Multimedia Authoring Periode 1 6.0 X_405057

Neural Networks Period 1 6.0 X_400132

Operating Systems Periode 4 6.0 X_405067

Parallel Programming Periode 1 6.0 X_400161

Performance Analysis of Communication Networks

Periode 1+2 6.0 X_400165

Project Agent Systems Periode 6 6.0 X_405089

Service Oriented Design Periode 1 6.0 X_405061

Software Architectuur Periode 2 6.0 X_400170

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-Minor BIOINFORMATICA Compulsory optional courses Minor BIOINFORMATICA Compulsory courses

Minor BIOINFORMATICA Compulsory optional courses

Vakken:

Minor BIOINFORMATICA Compulsory courses

Vakken:

Compulsory Courses

Compulsory alongside the mentioned courses, are optional courses (51 credits) to complete 120 credits.

Vakken:

Biosystems Data Analysis Periode 3 6.0 X_437001

Literature Study Ac. Year (September) 6.0 X_400277

Structural Bioinformatics Periode 4 6.0 X_405019

Algorithms in Sequence Analysis Periode 2 6.0 X_405050 Bioinformatics for Translational Medicine Periode 5 6.0 X_405092 Fundamentals of Bioinformatics Periode 1 6.0 X_405052

Advanced Information Retrieval

Periode 4+5 6.0 X_418043

Behaviour Dynamics Periode 1+2 6.0 X_400113

Evolutionary Computing Period 1 6.0 X_400111

History and Philosophy of the Information Society

Periode 2 3.0 X_405043

Intelligent Web Applications Periode 4 6.0 X_405055

Knowledge Management and Modeling

Period 1+2 6.0 X_400125

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-History, philosophy & social aspects of science

The choice of one of these elective courses, has to

be discussed and agreed upon with the master coordinator or a personal mentor and approved by the Examination Board.

Research variant Cognitieve Science

Opleidingsdelen:

Individuele vakken

Recommended Optional Courses Compulsory Courses

AI Vervallen vakken 1-9-2011

Individuele vakken

Recommended Optional Courses

Vakken:

Research methods Periode 2, Periode 5 6.0 X_405085

Scientific Writing in English Ac. Jaar (september), Periode 4, Periode 5, Periode 6

3.0 X_400592

Advanced Statistics for Experimentation

Period 4 6.0 P_MADVSTA

Comparative Modeling 6.0 X_405091

Periode 5 6.0 X_405078

Human Ambience Innovation

Periode 2 6.0 X_405053

Memory and Memory Disorders

Period 2 6.0 P_MMEMORY

Model-based Intelligent Environments

Periode 3 6.0 X_405056

Perception Period 2 6.0 P_MPERCEP

Review Paper Period 3 6.0 P_MREVPAP

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-Compulsory Courses

Opleidingsdelen:

Masterproject

Vakken:

Masterproject

Students need to select one of the mentioned Master Projects.

Vakken:

Brain Imaging Period 5 6.0 P_MBRIMAG

Periode 2 3.0 X_405043 Human Information Processing Period 1 6.0 P_MHINFOP Knowledge Management and Modeling Period 1+2 6.0 X_400125

Neural Models of Cognitive Processes

Period 2 6.0 P_MNEUMOD

Scientific Writing in English Ac. Jaar (september), Periode 4, Periode 5, Periode 6 3.0 X_400592 Seminar Cognitive Neuroscience Period 1 6.0 P_MSEMCNS

Special Topics Cognitive Science

Ac. Jaar (september) 12.0 X_405090

Thinking and Deciding Period 2 6.0 P_MTHIDEC

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-AI Vervallen vakken 1-9-2011

History, philosophy & social aspects of science

Research variant CISO

After completion of this Master programme, the student - has an overview of the literature an practice in the area of organisation dynamics and self organisation

- has mastered methods and techniques for modelling various types of organisations and their dynamics

- is capable of constructing models of dynamic organisations with which can be simulated and experimented

- is capable of conducting application-directed AI research in combination with other fields of research.

Students of this programme can function in industry through a variety of different often management-related professions, within a diversity of institutions and companies, for example, in strategic management and organisation advising.

Audience: Students with an interest in analysing, modelling, simulating and experimenting with dynamics properties.

Opleidingsdelen:

Individuele vakken Master Thesis: Research Project Cognitive Science

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-Recommended Optional Courses Compulsory Courses

Individuele vakken

Recommended Optional Courses

Opleidingsdelen:

Vakken:

Minor Bioinformatica

Periode 4+5 6.0 X_418043

Cluster and Grid Computing Period 4 6.0 X_400362

Coding and Cryptography Periode 1 6.0 X_405041

Period 5 6.0 X_400127

Computer Graphics Period 2 6.0 X_400106

Concurrency and Multithreading

Periode 5 6.0 X_405064

Evolutionaire genetica 6.0 AB_470053

Game Theory [MC] Periode 2 5.0 X_418021

Performance Analysis of Communication Networks

Periode 1+2 6.0 X_400165

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-Opleidingsdelen:

Minor BIOINFORMATICA Compulsory optional courses Minor BIOINFORMATICA Compulsory courses

Minor BIOINFORMATICA Compulsory optional courses

Vakken:

Minor BIOINFORMATICA Compulsory courses

Vakken:

Compulsory Courses

Vakken:

Periode 5 6.0 X_405078

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-History, philosophy & social aspects of science

Research variant TAI

The graduate student of this Master programme is capable of applying techniques from Computer Science to problems of an Artificial

Intelligence nature, e.g., designing knowledge-based systems or multi- agent systems. This technically adapt graduate, furthermore, has learned proven AI techniques in the areas of machine learning, neural networks, knowledge representation, and evolutionary computing. Graduate students are well equiped for work in companies that create intelligent

applications.

Audience: the programme aims at students with a Bachelor in Computer Science, having an interest in Artificial Intelligence.

- compulsory optional choice 6 credits - optional courses 36 credits

Opleidingsdelen:

Individuele vakken

Recommended Optional Courses Compulsory Courses

Period 1+2 6.0 X_400125

Master Project Ac. Year (September) 30.0 X_400285

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-History, philosophy & social aspects of science

Individuele vakken

Recommended Optional Courses

Opleidingsdelen:

Vakken:

Periode 4+5 6.0 X_418043

Cluster and Grid Computing Period 4 6.0 X_400362

Coding and Cryptography Periode 1 6.0 X_405041

Period 5 6.0 X_400127

Computer Graphics Period 2 6.0 X_400106

Computer Networks Practical Periode 5+6 6.0 X_405072 Concurrency and Multithreading Periode 5 6.0 X_405064

Distributed Multimedia Systems

Period 4 3.0 X_405003

E-Business Innovation Periode 1 6.0 X_405051

Periode 5 6.0 X_405078

Logical Verification Ac. Year (September) 6.0 X_400115

Multimedia Authoring Periode 1 6.0 X_405057

Operating Systems Practical Periode 5+6 6.0 X_405071

Parallel Programming Practical Periode 2+3 6.0 X_400162 Performance Analysis of Communication Networks Periode 1+2 6.0 X_400165

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-Minor Bioinformatica

Opleidingsdelen:

Minor BIOINFORMATICA Compulsory optional courses Minor BIOINFORMATICA Compulsory courses

Minor BIOINFORMATICA Compulsory optional courses

Vakken:

Minor BIOINFORMATICA Compulsory courses

Vakken:

Compulsory Courses

Opleidingsdelen:

Compulsory Optional course (Software Engineering)

Vakken:

Seminar Attention Period 3 6.0 P_MSEMATT

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Compulsory Optional course (Software Engineering)

Students need to select at least one out of the following courses.

Vakken:

History, philosophy & social aspects of science

Research Variant Human Ambience

In the Master variant Human Ambience you learn on a detailed level how to model both mental and physiological processes of human functioning. For instance, you can learn how to model the mental and physical states associated with depression. Such models are then used in applications that support humans in their daily lives in a dedicated manner, also to enable the developed support systems to understand humans better. In the specialization phase of the master you can study relevant courses with respect to an application area (e.g. support of people during exercising, or elderly care) or a relevant scientific

Periode 2 3.0 X_405043

Period 1+2 6.0 X_400125

Master Project Ac. Year (September) 30.0 X_400285

3.0 X_400592

Software Asset Management

Period 1 6.0 X_400412

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-discipline (e.g. psychology, sociology, movement sciences, biomedical sciences, criminology, etc.). During you final Master project you will

then combine your domain knowledge with the knowledge of modeling such human processes.

Dr. S. Schlobach (International students) K room T-365 T +31 (0) 20 598 7678 E [email protected] Opleidingsdelen: Individuele vakken Keuzevakken Compulsory Courses

Individuele vakken

Keuzevakken

N.B. Students can compose an individual programme by selecting all optional courses from one specific discipline, but also by combining courses from different disciplines, which have a common application. Opleidingsdelen:

Optional courses Health

Optional courses Mental Fuctioning/Health Optional courses Movement

Optional courses Social Functioning/Networks Optional courses Criminology

Vakken:

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Optional courses Health

Vakken:

Optional courses Mental Fuctioning/Health

Vakken:

Optional courses Movement

Periode 5 6.0 X_405078

Health Promotion and Disease Prevention

Period 1 6.0 AM_470811

Health Psychology Period 2 6.0 AM_470730

Prevention of Mental Health Problems

Period 3 6.0 AM_470840

Advanced Statistics for Experimentation

Period 4 6.0 P_MADVSTA

Aging and Dementia Period 1+2+3+4 6.0 P_MAGINGD

Behavioral Methods Periode 1 6.0 P_MBEHMET

Brain Imaging Period 5 6.0 P_MBRIMAG

Human Information Processing

Period 1 6.0 P_MHINFOP

Memory and Memory Disorders

Period 2 6.0 P_MMEMORY

Neural Models of Cognitive Processes

Period 2 6.0 P_MNEUMOD

Neuropsychology of Cognitive Dysfunctioning

Period 2 6.0 P_MNCDYSF

Perception Period 2 6.0 P_MPERCEP

Seminar Attention Period 3 6.0 P_MSEMATT

Seminar Cognitive Neuroscience

Period 1 6.0 P_MSEMCNS

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Vakken:

Optional courses Social Functioning/Networks

Vakken:

Optional courses Criminology

Vakken:

Minor Bioinformatica

Opleidingsdelen:

Coordination Dynamics: principles and clinical applications

Period 2 3.0 B_CLINCORDYN

Dynamica van Lineaire Systemen

Periode 1 3.0 B_DYNAMICA

Energy Flow Models Period 2 3.0 B_ENERFLOW

Perception for Action Period 4 3.0 B_PERCACTION

Sport Biomechanics Periode 5+6 3.0 B_SPORTBIO

Advanced Network Analysis, Annual Seminar in Organizations Period 1 5.0 SANA-ASO_O Fysieke veiligheid en crisisbeheersing Periode 1 5.0 SFVCB_O Gedrag en communicatie in organisaties deel 1 5.0 SGCO1_O Interpersoonlijke communicatie deel 1 Periode 1 5.0 SIPC1_O

Organizational Space and Technology

Period 1 5.0 SOST_O

Sociale netwerken en organisaties

Periode 1 5.0 SSNOB_O

Misdaadanalyse Periode 5 6.0 R_Misd.anaC

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-Minor BIOINFORMATICA Compulsory optional courses Minor BIOINFORMATICA Compulsory courses

Minor BIOINFORMATICA Compulsory optional courses

Vakken:

Minor BIOINFORMATICA Compulsory courses

Vakken:

Compulsory Courses

Vakken:

Comparative Modeling 6.0 X_405091

Periode 2 3.0 X_405043 Human Ambience Innovation Periode 2 6.0 X_405053 Knowledge Management and Modeling Period 1+2 6.0 X_400125

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History, philosophy & social aspects of science

Advanced Information Retrieval

Inhoud vak

The course description is available on:

http://studiegids.uva.nl/web/uva/sgs/en/c/10048.html

Overige informatie

Opgave via https://www.sis.uva.nl tot 4 weken voor aanvang van het semester is verplicht

Course registration at the UVA is compulsory at least 4 weeks before the start of the semester via https://www.sis.uva.nl

Advanced Logic

Doel vak

The objective is to obtain a good understanding of modal logic and its use in computer science and artificial intelligence.

Inhoud vak

A thorough introduction to modal logics, and its applications in

computer science and artificial intelligence. We will select some themes from the book Modal Logics for Open Minds, by Johan van Benthem: basic

Model-based Intelligent Environments

Periode 3 6.0 X_405056

Scientific Writing in English Ac. Jaar (september), Periode 4, Periode 5, Periode 6 3.0 X_400592 Vakcode X_418043 (418043) Periode Periode 4+5 Credits 6.0 Voertaal Engels

Faculteit Faculteit der Exacte Wetenschappen

Vakcode X_405048 (405048)

Periode Periode 4

Credits 6.0

Voertaal Engels

Coördinator dr. R.D.A. Hendriks

Docent(en) dr. R.D.A. Hendriks, A. Polonsky

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modal logic and possible world semantics, bisimulation and invariance, modal definability, decidability, ... In particular we treat the modal logics most relevant to computer science and AI: temporal, dynamic and epistemic logic.

Onderwijsvorm

Weekly 2 lectures and 1 exercise class, for the duration of 7 weeks.

Toetsvorm

A written exam and assignments that can make half a point bonus.

Literatuur

Johan van Benthem, Modal Logics for Open Minds, CSLI Publications 2010.

Aanbevolen voorkennis

The bachelor course Logica en Modelleren (previously Inleiding Logica), or an equivalent introduction to first-order logic.

Doelgroep

mAI-KTIIA, mAI-TLI, mAI-C-var, mCS-FMSV

Advanced Network Analysis, Annual Seminar in Organizations

Course content

Advanced Network Analysis consists of three parts:

- A general introduction to the course including a brief summary of Social Networks and Organization (Bachelor 3; premasterclass) focusing on the technical aspects of social network analysis and basic network theory.

- An introduction to two or three advanced network related topics. In recent years the topics were Measurement of Social Capital,

Consequences of Social Network Positions, Antecedents of Networks, Network dynamics, and Actor-based Network Models. The content of the networks were often advice, friendship, conflict, on-line

relationships, etc.

- A computer practice UCINET (a social network analysis package) during which relatively simple network measures are calculated, but during which also an introduction to an advanced type of analysis is given, namely QAP regression.

Type of assessment

The clean sweep test is a written exam. Find the schedule on:

http://www.fsw.vu.nl/en/students/schedules/clean-sweep-tests/index.asp.

Course reading

Course code SANA-ASO_O (708700)

Period Period 1

Credits 5.0

Language of tuition English

Faculty Faculteit der Sociale Wetenschappen

Coordinator dr. G.G. van de Bunt

Teaching staff drs. M.C. de Klepper, dr. F. Agneessens

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A selection of books and/or articles are compulsory readings The exact list will be communicated via blackboard.

Entry requirements

Basic knowledge of social network analysis (as taught in B&O bachelor or BCO premaster)

Advanced Selforganisation

Course objective

To understand, simulate and analyse the behaviour and self-organization of complex systems. The student is able to explain, implement and recognize basic principles and properties of such systems.

Course content

This course is about the understanding of the behavior and self-

organization of complex systems: systems in which the interaction of the components is not simply reducible to the properties of the components. The general question the we address is: how should systems of very many independent computational (e.g. robotic or software) agents cooperate in order to process information and achieve their goals, in a way that is efficient, self- optimizing, adaptive, and robust in the face of damage or attack? We will look at natural systems that solve some of the same problems that we want to solve, e.g. adaptive path minimization by ants, wasp and termite nest building, army ant raiding, fish schooling and bird flocking, coordinated cooperation in slime molds, synchronized firefly flashing, evolution by natural selection, game theory and the evolution of cooperation. The course includes a practical part in which students implement a simulation of a self-organizing complex system and conduct structured experimental analysis with this simulation.

Form of tuition

Theory in lectures and practice in labs.

Report including description of simulation and experimental analysis.

Course reading

Schut M.C., Scientific Handbook for Simulation of Collective Intelligence, 2007. Available at http://sci. collectivae. net/.

Target group

mAI-CIS, mAI-HA, mAI-KTIIA, mAI-TAI, mBA, mBA-D, mCS-TAI, mPDCS

Remarks

Course code X_400434 (400434)

Period Period 2

Credits 6.0

Faculty Faculteit der Exacte Wetenschappen

Coordinator dr. M.C. Schut

Teaching staff dr. M.C. Schut

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More information available on BlackBoard. This is a project- oriented course and therefore students will be expected to have basic

programming skills.

Advanced Statistics for Experimentation

To acquire knowledge of and insight into statistics in order to be able to apply these techniques and read associated literature at a level relevant for research in cognitive neuropsychology.

Course content

This course provides a theoretical overview and detailed practical knowledge concerning statistical analyses of social psychological data. After an introduction of the general linear model, with emphasis on estimation of effect sizes and hypothesis testing, the course

concentrates on applications of the model, such as analysis of variance, regression analysis, path analysis, and logistic regression. Along with these techniques, issues such as mediation, moderation, and hypothesis testing are considered. The aim of the course is to enable student to plan, execute, and interpret appropriate statistical analyses for applied and experimental research data. Because the application of advanced statistical techniques is central to the course, students will have several assignments to analyze existing data sets, and interpret the results.

Form of tuition

- H143 J., D144 P., West, S.I144G., & Aiken, L.S. (2003), Applied Multiple regression / I145G146correlation; analysis for the behavioural I147sciences (3rd ed. ) Hillsdale, NJ: Erlbaum;

- H150Additional material provided during the course.

Assignments and final examination.

Course reading

To be announced.

Remarks

Admission conditions: Statistics (or a similar course).

Aging and Dementia

Course code P_MADVSTA (815097)

Period Period 4

Credits 6.0

Faculty Faculteit der Psychologie en Pedagogiek

Coordinator dr. M. Gallucci

Teaching staff dr. M. Gallucci

Teaching method(s) Lecture

Course code P_MAGINGD (815181)

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Provide an advanced course on the neuropathological, cognitive and behavioural consequences of aging and age- related neurodegenerative diseases, in particular dementia.

Course content

The neuropathology characteristic for aging and various subtypes of dementia will be related to specific functional neuronal circuits. Based on these functional neuronal circuits the clinical outcome in terms of cognitive and behavioural disorders will be explained. Specific attention will be given to the relationship between dementia and motor activity and between dementia and pain experience.

Form of tuition

Plenary lectures, with an emphasis on interaction with the students.

Open-end questions.

Course reading

E. Scherder. Aging and Dementia. Neuropsychology, motor skills and pain. VU Uitgeverij.

Remarks

This course will be lectured twice:

- In periode 1 the course is sceduled for the Research master Cognitive neuropsychology.

- In period 3 the course is sceduled for the Master psychology, trace Clinical neuropsychology.

Due to technical problems self-registration for the course in period 3 is not able anymore after registration for period 1-courses has been closed. Students of the Master Psychology that will follow the course in period 3 may register at the student service desk. If the registration takes place ultimately four weeks before the start of period 3, no admission fee will be charged.

Students who have followed the course “Neuropsychological Disorders: Development and Course II” (course code 813088) during their Bachelor Psychology at VU University are refused the exam of "Aging and

Dementia". Instead, these students are allowed to follow one of the two courses of the Research master Cognitive Neuropsychology,

"Neuropsychology of Cognitive Dysfunctioning" or "Perception".

Algorithms in Sequence Analysis

Credits 6.0

Coordinator prof. dr. E.J.A. Scherder

Teaching staff prof. dr. E.J.A. Scherder

Teaching method(s) Lecture

Vakcode X_405050 (405050)

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Doel vak

Have you ever wondered how we can track a gene across 3 billion years of evolution? Sequence alignment can be used to compare genes from humans and bacteria, using a dynamic programming algorithm. Here we focus on algorithms that can be applied to real scientific problems in biology. Students will obtain an in depth knowledge about the theory of sequence analysis methods.

Students will also develop understanding and skills to apply the

algorithms to protein and DNA sequences. We would like to stress that no biological knowledge is required to enter this course.

Goals

- At the end of the course, the student will be aware of the major issues, methodology and available algorithms in sequence analysis. - At the end of the course, the student will have hands-on experience in tackling biological problems using sequence analysis algorithms. - At the end of the course, the student will be able to implement several of the most important algorithms in sequence analysis.

Inhoud vak

Theory:

- Dynamic programming, database searching, pairwise and multiple alignment, probabilistic methods including hidden markov models,

pattern matching, entropy measures, evolutionary models, and phylogeny. Practical:

- Programming own alignment algorithm based on dynamic programming - Reverse translation and dynamic programming

- Homology searching and pattern recognition using biological and disease examples

- Multiple alignment of biological sequences - Entropy-based functional residues prediction

- Programming own implementation of Hidden Markov Models

Onderwijsvorm

13 Lectures: 2 two-hour lectures per week 6 computer practicals: two hours per week

Toetsvorm

The final grade for this course will consist of 50% practical work (see above) and 50% theoretical assessment.

The theoretical assessment will be an oral or written exam (depending on number of students).

Literatuur

Course material on bb.vu.nl

Books: Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.. Biological Sequence Analysis. Cambridge University Press, 1998, 350 pp., ISBN 0521629713.

Credits 6.0

Voertaal Engels

Coördinator prof. dr. J. Heringa

Docent(en) prof. dr. J. Heringa

(28)

Recommended reading: Marketa Zvelebil and Jeremy O. Baum Understanding Bioinformatics Garland Science 2008 ISBN-10: 0-8153-4024-9

Vereiste voorkennis

Bachelor in any science discipline (including medicine).

Basic programming skills and an interest in biological problems.

Doelgroep

mAI-CIS, mAI-HA, mAI-KTIIA, mAI-TAI, mBio, mCS-FMSV, mCS-HPDC, mCS-IWT, mCS-MM, mCS-SE, mCS-TAI

Signing up via bb.vu.nl is mandatory. The course is taught in English.

Automated Reasoning in AI

Since its early days Artificial Intelligence has employed logic as a mean to provide generic solutions for computationally and conceptually difficult practical problems.

The aim of the course is to make the students familiar with a number of popular logic- based representation and reasoning mechanisms for Artificial Intelligence. Furthermore, students should have the

capability to transfer the learned techniques to other problems and to other representation mechanisms.

Course content

The course will be structured in three modules. In each of these modules a practical problem will be introduced, a logic- based representation proposed, and the basic techniques for automated reasoning in this language studied in a practical, hands on, way. In a nutshell, we plan to cover:

- propositional Logic for scheduling, and satisfiability checking with Davis Putnam;

- Allen's interval logic for Planning, with constraint propagation in Temporal Constraint Networks;

- description logics for classification, with Tableau calculi for subsumption.

Form of tuition

In period 5 there will be lectures and practical sessions, plus significant time for self- study and practical work. In period 6 there will be regular meetings to support for the work on a larger project.

Course code X_400389 (400389)

Period Period 5

Credits 6.0

Coordinator dr. K.S. Schlobach

Teaching staff prof. dr. F.A.H. van Harmelen

(29)

3 practical assignments

Course reading

Selected scientific papers.

Basic knowledge in logic is an advantage, but not required, as is some familiarity with programming.

Target group

mAI

Remarks

For further information see the AR in AI blackboard site.

Behavioral Methods

This course will not be taught anymore from 2011/12. A transitional regulation is applied to students that have attended this course in 2010/11 or earlier, but not succeeded yet.

Students who attended this course in 2010/11, but did not successfully meet the passing-standard, are offered two opportunities to write the exam to complete the course Behavioral Methods in 2011/12. The first opportunity will be scheduled to take place at the end of period 1, the second opportunity will be scheduled to take place at the end of period 2. Take a look at VUnet or www.rooster.vu.nl for the exact time

schedule.

Behaviour Dynamics

Doel vak

To learn how to identify, specify and predict different types of

behaviour; to understand how externally observable behaviour emerges from internal mechanisms; to be able to construct computational

Vakcode P_MBEHMET (815096)

Credits 6.0

Voertaal Engels

Faculteit Faculteit der Psychologie en Pedagogiek

Coördinator dr. L.J.F.M. van Zoest

Vakcode X_400113 (400113)

Periode Periode 1+2

Credits 6.0

Voertaal Engels

Coördinator dr. O. Sharpanskykh

Docent(en) prof. dr. J. Treur

(30)

behavioural models and to perform analysis based on these models using software tools

Inhoud vak

Behavioural dynamics occurs in different forms, contexts and complexity. During the course examples of such behaviour are studied coming from software systems (e.g., knowledge- and agent-based systems), cognition (e.g., the use of beliefs, desires and intentions, complex reasoning tasks) and organisation theory (e.g., organisational change). The dynamics of behaviour of such systems is analysed (including verification and validation), modelled and simulated in this course using different techniques and tools.

Onderwijsvorm

Combinations of lectures, practical assignments, and presentations.

Toetsvorm

Examination and practical assignments. Both grades should be at least 5. 5 to pass the course.

Literatuur

Online reader.

Knowledge in mathematical logics (in particular, first-order predicate logic), logic programming

Conceptual modelling skills; knowledge on agent-based systems

Bioinformatics for Translational Medicine

Doel vak

Observations from biological high-throughput experiments will allow us to improve diagnosis and give a personalised treatment plan for patients. However, integrating data from several sources and using this data for predictions is non-trivial.

This is a theoretical and practical Bioinformatics course on

computational methods for Translational Medicine; we will focus on Bioinformatics algorithms that are used to predict the clinical outcome for patients and analysis methods to obtain deeper understanding of complex diseases, by combining data from various high-throughput experiments such as proteomics, microarrays and next-generation sequencing as well as existing biological databases.

Vakcode X_405092 ()

Credits 6.0

Voertaal Engels

Coördinator prof. dr. J. Heringa

Docent(en) prof. dr. J. Heringa

(31)

goals

• At the end of the course, students will be aware of

Bioinformatics methods that are applicable to the area of Translational Medicine.

• Students should be able to combine these methods to come to a creative solution to get new insights from large scale biological experiments.

• At the end of the course, students will have hands-on experience in handling large biological datasets, and will understand the

complexity of the biological data both from high-throughput experiments and existing biological databases.

• The student will become familiar with a few in depth research topics that lie within the expertise area of several (Bioinformatics) researchers at the VU, UvA and VUMC.

Inhoud vak

Theory

• proteomics (mass spectrometry), genomics, gene regulation, signalling, microarray experiments, protein-protein interactions, and data-mining, next-generation sequencing, pattern recognition, ontologies, and GRID computing, Petri nets.

Practical

• Assignment biological data clustering (in R)

• Assignment gene regulation / signalling network modelling using Petri nets.

• Assignment classification of tumor data

Onderwijsvorm

• 13 Lectures (2 two-hour lectures per week)

• 12 computer practicals (2 two-hour sessions per week)

Toetsvorm

The final grade for this course will consist of 50% practical work (see above) and 50% theoretical assessment.

Theoretical assessment: (50%)

• Oral or written exam (depending on number of course students). • As part of the exam a research paper on a Bioinformatics method needs to be analysed in detail.

• You will be prepared for you exam through exercises and paper discussion during the lectures.

Literatuur

• course material on bb.vu.nl

• Marketa Zvelebil and Jeremy O. Baum Understanding Bioinformatics Garland Science 2008 ISBN-10: 0-8153-4024-9

• Bachelor in any science discipline (including medicine), or third-year BSc students.

• Basic programming skills (R) and an interest in biological problems.

(32)

Doelgroep

mAI-CIS, mAI-HA, mAI-KTIIA, mAI-TAI, mBio, mCS-FMSV, mCS-HPDC, mCS-IWT, mCS-MM, mCS-SE, mCS-TAI

Signing up via bb.vu.nl is mandatory. The course is taught in English.

• Compulsory course for students in MSc of Bioinformatics. • Optional course for students with Bachelor Physics, Chemistry, Mathematics, Computer Science, Biology, or Biomedical Sciences.

Biosystems Data Analysis

Inhoud vak

The course description is available at

http://studiegids.uva.nl/web/uva/sgs/en/c/12461.html

Doelgroep

mAI, mBio, mCh, mCS

Opgave via https://www.sis.uva.nl tot 4 weken voor aanvang van het semester is verplicht

Course registration at the UVA is compulsory at least 4 weeks before the start of the semester via https://www.sis.uva.nl

Brain Imaging

To learn how various brain imaging techniques are used in modern neuro- cognitive research.

Course content

The course will treat physical principles, recording apparatus, and practical applications of the four major brain imaging techniques: EEG,

Vakcode X_437001 (437001)

Credits 6.0

Voertaal Engels

Coördinator dr. D. Molenaar

Course code P_MBRIMAG (815103)

Period Period 5

Credits 6.0

Coordinator dr. D.J. Heslenfeld

Teaching staff dr. D.J. Heslenfeld

(33)

MEG, MRI, PET, with an emphasis on EEG and MRI. These techniques will be discussed in detail and live demonstrated. We will visit the various labs, and students will perform a small research project of their own. This includes recording and analyzing your own brain imaging data in small supervised groups.

Form of tuition

Lectures and obligatory practicals.

Written examination

Course reading

- Luck, S (2005) An introduction to the Event -Related Potential Technique Cambridge, MA: MIT Press

- Huettel, S et al (2009) Functional Magnetic Resonance Imaging; (2 nd. ed. ) Sunderland, MA: Sinauer;

Remarks

Language: tuition in English

MRI practicals will take place on Wednesdays in the afternoon/evening.

Cluster and Grid Computing

Students shall both explore the area of Cluster and Grid Computing and develop their skills in critical assessment of scientific literature.

Course content

Both Cluster and Grid computing are areas of rapid technical developments. Many technical developments are still in flux. We investigate resource management and scheduling, remote data access, network and other performance issues, as well as software architecture and programming models for grids.

Form of tuition

Introductory lecture, followed by a seminar part and practical

programming assignments. In the seminar part, students explore topic areas of Cluster and Grid Computing in small groups, present their findings in a presentation session and prepare a report. The practical programming assignments are to be addressed individually.

Both parts contribute 50% to the grade: (i) seminar presentation and report (ii) programming assignments

Course code X_400362 (400362)

Period Period 4

Credits 6.0

Coordinator dr. ing. T. Kielmann

Teaching staff dr. ing. T. Kielmann

(34)

Course reading

Various research articles as available online.

Parallel Programming (code 400161)

Target group

mPDCS, mCS-HPDC

Remarks

Participation in the course is limited; priority is given to students

of the M. Sc. programme in Parallel and Distributed Computer Systems, and to students following the HPDC specialization of; the Msc in

Computer Science. Registration for the course is required before the first lecture by sending email to the lecturer; first come first serve.

Coding and Cryptography

Doel vak

To give an introduction the theory of error correcting codes and to cryptography.

Inhoud vak

This course provides a thorough introduction to the theory of error correcting codes, and to cryptography. It is aimed especially at students of Computer Science. For error correcting codes we shall include cyclic codes, BCH codes, Reed-Solomon codes and burst error correction. For cryptography we discuss some modern public key cryptography (e.g., RSA, ElGamal, DSA).

Onderwijsvorm

Lectures and exercise classes

Toetsvorm

Written exam and homework

Literatuur

We shall be working from "Coding theory and cryptography, the

essentials" by Hankerson, Hoffman, Leonard, Lindner, Phelps, Rodger and Wall (second edition, revised and expanded).

"Algebra en Discrete Wiskunde 1", "Inleiding Codering en Discrete Wiskunde", or equivalent. Doelgroep Vakcode X_405041 (405041) Periode Periode 1 Credits 6.0 Voertaal Engels

Coördinator prof. dr. R.M.H. de Jeu

Docent(en) prof. dr. R.M.H. de Jeu

(35)

mAI-CIS, mAI-TAI, mCS-FMSV, mCS-HPDC, mCS-IWT, mCS-MM, mCS-SE, mCS-TAI, mMath, mPDCS

Comparative Modeling

Computer and Network Security

Introductory course on security with a scope that includes systems work. At the end of the course students will understand the basic notion of memory corruption attacks (buffer overflows, format strings, etc), SQL injection, cross-site scriting attacks, and other vectors used by computer hackers. Also, they will be able to understand and apply cryptography.

Course content

The course covers a wide spectrum of security issues. We explicitly aim wider than cryptography, as we want to show students how hackers penetrate systems. Part of the course will be hands-on: in lab assignments, student will carry out and investigate attacks in a controlled environment. This involves programming at the both the highest and lowest levels (say SQL and assembly). However, we will also discuss cryptography and trust infrastructures.

Form of tuition

Lectures and practical assignments

Written exam (50%) and practical assignments (50%).

Course reading

No set book. All material will be made available during the course.

No formal requirements, except a keen interest. Programming experience in C strongly recommended.

Vakcode X_405091 ()

Credits 6.0

Voertaal Engels

Coördinator dr. T. Bosse

Course code X_400127 (400127)

Period Period 5

Credits 6.0

Coordinator prof. dr. ir. H.J. Bos

Teaching staff prof. dr. ir. H.J. Bos

(36)

Target group mCS, mPDCS Remarks http://www.few.vu.nl/~herbertb/sec/

Computer Graphics

The students shall get theoretical insights and practical knowledge that allows them to implement graphics applications and to understand how such applications get executed on current graphics hardware.

Course content

the course has a top-down structure, starting with the applications. Topics of the lecture are:

- graphics programming with OpenGL - color, input, interaction

- transformations (translation, rotation, scaling, shear) - 3-dimensional viewing (projections, perspective) - light and shading

- discrete techniques, buffers, texture mapping - modeling (object hierarchies, scene graphs)

- advanced topics (curves and surfaces, programmable shaders)

Form of tuition

Class sessions consist of lecture elements and practical exercises. Towards the end of the course, an individual programming project will be done by which students apply what they have learned throughout the class. All programming exercises and the project will be based on

OpenGL and the programming language Java. It is highly recommended that students bring their own laptop computer to the class sessions in order to perform the practical exercises.

Written exam (code 4001061) and programming project (code 4001062). The project contributes 2/3 to the final grade. The exam contributes 1/3.

Both parts need to be graded sufficient or better in order to get the credit

points for the course 400106.

Course reading

Edward Angel, Interactive Computer Graphics, 5th edition. Addison Wesley.

Course code X_400106 (400106)

Period Period 2

Credits 6.0

Coordinator dr. ing. T. Kielmann

Teaching staff dr. ing. T. Kielmann

(37)

Project Programmeren (400559)

Target group

mCS, mPDCS

Remarks

The classes are given in period 2; the programming project is continued in period 3.

Computer Networks Practical

Doel vak

Put concepts of Computer Networks and Operating Systems into practice, in the context of smartphones.

Inhoud vak

Low-level programming assignments on smartphones, requiring the thorough understanding of operating systems and network concepts.

Onderwijsvorm

Practical computer work

Toetsvorm

Practical computer work.

Mandatory: Computer Networks (400487).

Strongly recommended: Operating Systems (400011)

Doelgroep

mAI-TAI, mCS-FMSV, mCS-HPDC, mCS-IWT, mPDCS

Assignments can be submitted until the end of August. Students who have not taken the Operating Systems course at the VU are strongly advised to do so, or to study and experiment with the corresponding material

themselves.

Concurrency and Multithreading

Vakcode X_405072 (405072)

Periode Periode 5+6

Credits 6.0

Voertaal Engels

Coördinator dr. S. Voulgaris

Docent(en) ir. M.P.H. Huntjens

Lesmethode(n) Hoorcollege

Vakcode X_405064 (405064)

(38)

Doel vak

This course provides a comprehensive presentation of the foundations and programming principles for multicore machines.

Inhoud vak

Shared memory, mutual exclusion, synchronization operations, concurrent data structures, scheduling, transactional memory, multithreaded

programming.

Onderwijsvorm

Lectures: 4 hours per week, exercise classes: 4 hours per week.

Toetsvorm

Written exam (which counts for 70% of the final mark) and two programming assignments (which together count for 30% of the final mark).

Literatuur

Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Morgan Kaufmann, 2008.

Doelgroep

mAI-CIS, mAI-KTIIA, mAI-TAI, mCS-FMSV, mCS-HPDC, mCS-IWT, mCS-MM, mCS- SE, mCS-TAI, mPDCS

The lectures and written exam of the BSc and MSc variant of Concurrency & Multithreading coincide.

The difference is that the BSc variant has two small programming assignments, while the MSc variant has one small and one large programming assignment.

The MSc variant of this course cannot be followed by students that included the BSc variant in their BSc program.

Coordination Dynamics: principles and clinical applications

Voertaal Engels

Coördinator prof. dr. W.J. Fokkink

Docent(en) prof. dr. W.J. Fokkink

Lesmethode(n) Hoorcollege, Werkcollege

Course code B_CLINCORDYN (900666)

Period Period 2

Credits 3.0

Faculty Faculteit der Bewegingswetenschappen

Coordinator dr. M. Roerdink

Teaching staff dr. M. Roerdink

(39)

The student is acquainted with the principles, concepts and methods of coordination dynamics, as used in the study of basic and pathological movements. The student can explain these aspects of coordination dynamics in a qualitative manner and is able to indicate how they may contribute to clinical diagnosis and intervention.

Course content

The coordination dynamics approach is pursued to study how patterns of coordinated movement come about, persist and change as a function task constraints, learning, expertise and pathology. Coordination dynamics is governed on the one hand by principles of self- organization, and on the other hand by intentionality, perceptual information and explicit knowledge.

Coordination patterns exist at multiple levels:

1. dynamics within or between body segments of a moving person; 2. dynamics between moving segments of multiple persons and 3. dynamics between person and external events, as well as between persons.

The first part of the course provides an overview of the principles, concepts and methods of coordination dynamics.

The second part of the course focuses on the application of coordination dynamics in a clinical (rehabilitation) setting, with specific emphasis on pathological gait and interventions based on environmental coupling. Specifically, coordination dynamics provides a framework to study the nature of healthy and pathological movements by assessing stability and loss of stability of coordination patterns,

thereby assisting the diagnosis and evaluation of rehabilitation-

induced changes in coordination. Furthermore, coordination dynamics may promote therapeutic interventions based on environmental coupling, aimed at facilitating desired coordination patterns and/or stabilizing existing unstable coordination patterns.

Form of tuition

Amount of contact hours, divided in: Lectures: 8 * 1.75 hrs

Laboratories: 2 * 2.00 hrs

Computer Practicals: 5 * 2.00 hrs Exam: 2.75 hrs

Part 1: Principles of coordination dynamics

- Lecture 1: How nature handles complexity: self-organization of behavior

- Lecture 2: Coordination dynamics at multiple levels - Lecture 3: Tools and methods of coordination dynamics - Laboratory 1: Relative phase and phase transitions in action - Practical 1: Analyses of rhythmic interlimb coordination - Practical 2: Analyses of rhythmic sensorimotor coordination Part 2: Clinical applications of coordination dynamics

- Lecture 4: Introduction to clinical coordination dynamics - Lecture 5: Interventions based on environmental coupling - Laboratory 2: Clinical coordination dynamics in action

- Practical 3: Functional changes in interlimb interactions following stroke

- Practical 4: Pathological gait modulation with visual and acoustic cues

- Lecture 6: Coordination dynamics and pathological gait - Lecture 7: Coordination dynamics in the future