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MA PhD

Last updated: 12.07.2021

Course Level University ECTS Leader Description
IN4050 – Introduksjon til kunstig intelligens og maskinlæring MA UiO 10 Tønnes Nygaard

Dette emnet gir en grunnleggende introduksjon til maskinlæring (ML) og kunstig intelligens (AI). Med en algoritmisk tilnærming gis studentene en praktisk forståelse av metodene som gjennomgås, ikke minst gjennom egen implementering av flere av metodene. Emnet dekker veiledet klassifikasjon basert på for eksempel kunstige nevrale nettverk (dyp læring), i tillegg til ikke-veiledet læring (klyngeanalyse), regresjon, optimalisering (evolusjonære algoritmer og andre søkemetoder) og forsterkende læring, samt design av eksperimenter og evaluering.

Course Leader: Tønnes Nygaard
Email: tonnesfn@ifi.uio.no

FYS-STK4155 – Anvendt dataanalyse og maskinlæring MA UiO 10 Morten Hjorth-Jensen

Emnet gir en innføring i en rekke sentrale algoritmer og metoder, som er viktige for studier av statistisk dataanalyse og maskinlæring. Emnet er prosjektbasert, og gjennom de ulike prosjektene introduseres studentene for grunnleggende forskningsproblemer innen disse feltene, med sikte på å gjengi moderne vitenskapelige resultater. Studentene lærer å utvikle og strukturere større koder for å studere disse systemene, bli kjent med datafasiliteter og lærer hvordan håndtere store vitenskapelige prosjekter. God vitenskapelig og etisk oppførsel vektlegges gjennom hele emnet.

Course Leader: Morten Hjorth-Jensen
Email: Morten.hjorth-jensen@fys.uio.no

TEK5040 – Dyp læring for autonome systemer MA UiO 10 Narada Dilp Warakagoda

Emnet tar for seg avanserte algoritmer og arkitekturer for dyp læring med nevrale nettverk. Emnet gir en innføring i hvordan teknikker basert på dyp læring kan anvendes i konstruksjon av viktige deler av avanserte autonome systemer som eksisterer i fysiske miljøer og cybermiljøer.

Course Leader: Narada Dilp Warakagoda
Email: n.d.warakagoda@its.uio.no

JUS5671 – Legal Technology: Artificial Intelligence and Law - University of Oslo MA UiO 10 Malcolm Langford

Legal Technology refers to the use of technology, software and computer analytics to provide legal services and justice. It is increasingly transforming legal practice and institutions and the nature of law and research. The most prominent development is the rise of computational applications in artificial intelligence in legal fields diverse as asylum, contracts, policing and finance. Moreover, creative uses of digital platforms and blockchain technology are providing new possibilities in dispute resolution, legal registries and private law orderings.

Course Leader: Malcolm Langford
Email: malcolm.langford@jus.uio.no

IN-STK5000 – Adaptive metoder for data-baserte beslutninger MA UiO 10 Christos Dimitrakakis

I dette emnet vil du lære om systemer som samler og prosesserer data på en adaptiv måte, for å støtte beslutninger, enten autonomt, eller sammen med mennesker. Emnet bruker sentrale prinsipper innen maskinlæring, kunstig intelligens, databaser, og parallellregning på realistiske problemstillinger knyttet til sikkerhet, reproduserbarhet, transparens, personvern, og rettferdighet.

Course Leader: Christos Dimitrakakis
Email: chridim@ifi.uio.no

IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems - Universitetet i Oslo MA UiO 10 Jim Tørresen

The course covers various methods within artificial intelligence (AI) and machine learning (ML), and their applications. Examples include algorithms for search, optimization and classification, which to a large extent consist of bio-inspired approaches.

Course Leader: Jim Tørresen
Email: jimtoer@ifi.uio.no

TEK5010 – Multiagent-systemer MA UiO 10 Hans Jonas Fossum Moen

Emnet gir deg en innføring i systemer med flere agenter/enheter/roboter som er gjensidige avhengig av hverandres oppførsel for å kunne karakterisere egen- eller systemytelse. Emnet vil ta for seg teorier for både strategisk interaksjon mellom ikke-samarbeidende agenter og mer eksplisitt koordinering av agenter som samarbeider i komplekse, distribuerte miljø. Spill-teori og sverm-intelligens er sentrale deler av emnet.

Course Leader: Hans Jonas Fossum Moen
Email: h.j.f.moen@its.uio.no

ITLED4410 – Digitaliseringsteknologi og prosessinnovasjon MA UiO 5 Egil Øvrelid

Hvordan kan din virksomhet bruke nye digitaliseringsteknologier som Internet of things (IoT), Kunstig intelligens/Maskinlæring, Robot Process Automation (RPA) for å innovere? Emnet gir en innføring i ulike digitale teknologier med et spesielt fokus på hvordan disse kan utnyttes for produkt og tjenesteinnovasjon. Spesifikt tar vi opp tema som kontinuerlig utvikling, tverrfaglige team, BizDev & DevOps, arkitekturer for applikasjonsutvikling og bruk av APIer.

Course Leader: Egil Øvrelid
Email: egilov@ifi.uio.no

IN5030 – Protokoller og ruting i Internett MA UiO 10 Tor Skeie

Forelesningene i dette emnet gis av fageksperter både fra industrien og akademia, hvor følgende temaer undervises: Grunnleggende Wi-Fi teknologi, IP router-arkitektur, Big Data og Cloud løsninger, Software Defined Networkning (SDN), Multicast kommunikasjon, Quality of Service, Grunnleggende optisk kommunikasjon, Internet of Things (IoT), Protokoller for multimedia applikasjoner, Cybersikkerhet, og bruk av kunstig intelligens (AI) og maskinlæring (ML) til trafikkontroll.

Course Leader: Tor Skeie
Email: tskeie@ifi.uio.no

MEVIT4311 – AI Innovations and chatbots. Understanding media innovations in a new age - University of Oslo MA UiO 10 Petter Bae Brandtzæg

The course MEVIT4311 - AI Innovations and chatbots will help you understand and assess innovations in the media sector with a particular focus on the latest developments within AI and chatbots. You will learn to evaluate innovation strategies, and to suggest new innovation strategies for companies. A practical task will be to develop and innovate a new chatbot service.

Course Leader: Petter Bae Brandtzæg
Email: p.b.brandtzag@media.uio.no

ITEVU4130 – Digital Twins for Science and Applications MA UiO 25 NA

This course introduces the digital twin as an integrating framework for data and computational science, and introduces the concept of the digital twin and discusses its history and relationship to data science.

Course Leader: NA
Email: NA

IN4060 – Semantiske teknologier MA UiO 10 Jieying Chen

"Semantic Web" (SW) er en spennende ny utvikling for fremtidens WWW. Samtidig brukes metodene og standardene utviklet for SW i økende grad for å utveksle og integrere data i industri og offentlig sektor. Semantiske teknologier utgjør en fascinerende kombinasjon av web-teknologi, databaseteknologi, modellering, formell logikk, og kunstig intelligens.

Course Leader: Jieying Chen
Email: jieyingc@ifi.uio.no

IN-STK5100 – Reinforcement Learning and Decision Making Under Uncertainty MA UiO 10 Christos Dimitrakakis

This course gives a firm foundation to reinforcement learning and decision theory from mainly a statistical, but also a philosophical perspective. The aim of the course is two-fold. Firstly, to give a thorough understanding of statistical decision making, Markov decision processes, automatic experiment design, and the relation of statistical decision making to human decision making. Secondly, to relate the theory to practical problems in reinforcement learning and artificial intelligence through algorithm design, implementation and a group project in reinforcement learning.

Course Leader: Christos Dimitrakakis
Email: chridim@ifi.uio.no

IN5400 – Maskinlæring for bildeanalyse MA UiO 10 Alexander Binder

Emner gir en innføring i teorien bak sentrale maskinlæringsalgoritmer som brukes i bildeanalyse. Videre beskrives utvalgte metoder og verktøy for dyp læring.

Course Leader: Alexander Binder
Email: alexabin@ifi.uio.no

MIEVU4030 – Metoder for beregning av usikkerhet i statistikk og maskinlæring MA UiO 2.5 NA

Dette emnet handler om metoder for å kvantifisere og beregne usikkerhet i statistikk og maskinlæring. Emnet vil gi en introduksjon til tradisjonelle metoder innen statistisk inferens og beregning av usikkerhet, men hovedfokuset vil være på bootstrapping.

Course Leader: NA
Email: NA

STK-IN4300 – Statistiske læringsmetoder i Data Science MA UiO 10 Riccardo De Bin

Emnet fokuserer på metoder i moderne dataanalyse både fra et praktisk og fra et teoretisk rammeverk. Slike metoder, kalt maskinlæring eller statistisk læring, gjør færre antagelser enn klassiske metoder. Følgelig spiller data en større rolle i identifisering av strukturer og sammenhenger. Emnet starter med klassiske metoder og dekker videre mer avanserte prosedyrer, spesifikt designet for å takle moderne datautfordringer som økende kompleksitet og store mengder av informasjon (stordatasituasjoner).

Course Leader: Riccardo De Bin
Email: debin@math.uio.no

JUS5690 – Robot Regulation MA UiO 10 Tobias Mahler

This course examines how robots and artificial intelligence are regulated de lege lata, and tracks the discourse about the need for new law (de lege ferenda). It is far from clear how society should respond to the emergence of these technologies, and students should think creatively about these questions. The course also tracks the development of soft law, such as codes of conduct for robot engineers.

Course Leader: Tobias Mahler
Email: tobias.mahler@jus.uio.no

TEK4030 – Control of Manipulators and Mobile Robots MA UiO 10 Kim Mathiassen

This course gives you a comprehensive understanding of control methods for robotic arms, known as manipulators, both for low-level control and planning of the robot's movements. You will also get detailed knowledge of mobile robots. This includes the characteristics of mobile robots and how to plan and follow trajectories. The focus of the course will be first to learn the theory and then employ this theory in practice, both in simulations and in the lab.

Course Leader: Kim Mathiassen
Email: kim.mathiassen@its.uio.no

TEK5020 – Mønstergjenkjenning MA UiO 10 Idar Dyrdal

Emnet gir en grunnleggende innføring i mønstergjenkjenning, med vekt på klassifiseringsteori og maskinlæring. Temaer som gjennomgås er Bayesisk beslutningsteori, klassifikatorer og klassifiseringssystemer, ledet læring, parametriske og ikke-parametriske metoder, lineære og generaliserte diskriminantfunksjoner, egenskapsutvelging og feilrateestimering, dimensjonalitetsproblemer, ikke-ledet læring og klyngeanalyse. Mønstergjenkjenning brukes ofte i sammenheng med bilde- og signalanalyse, og vil derfor være nyttig for mange studenter innenfor disse fagene.

Course Leader: Idar Dyrdal
Email: idar.dyrdal@its.uio.no

ITEVU4120 – Introduksjon: Fra data til innsikt MA UiO 25 NA

Emnet gir en oversikt over metoder og teknologier for innsamling, forvaltning, foredling og bruk av data i beslutningsprosesser og innen kunstig intelligens. Emnet er en overordnet innføring og danner grunnlag for en serie med emner med mer fordypning på utvalgte områder, f.eks. innen maskinlæring/dyplæring og representasjon av kunnskap gjennom digitale tvillinger. Emnet vi ha et særskilt fokus på betydningen av data og maskinlæring for grønn omstilling og bærekraft.

Course Leader: NA
Email: NA

STK4011 – Statistisk inferensteori MA UiO 10 Johan Pensar

Emnet utdyper og utvider inferensteorien fra tidligere emner. Spesielt behandles anvendelser på punktestimering og hypotesetesting.

Course Leader: Johan Pensar
Email: johanpen@math.uio.no

TEK4040 – Mathematical Modelling of Dynamic Systems MA UiO 10 Anders Rødningsby

The main part of the course presents the mathematics to describe the dynamic of solid bodies. This includes how vectors and linear operators are represented by matrices. Then, the direction cosine matrices and rotation matrices are defined and interpreted, and the direction cosine matrix differential equation, rotation matrix representations and relative motion are presented.

Course Leader: Anders Rødningsby
Email: anders.rodningsby@its.uio.no

IN5590 – Rapid Prototyping of Robotic Systems MA UiO 10 Mats Erling Høvin

IN5590 provides an introduction to computer aided design (CAD), rapid prototyping (3D printing) and computer numerical control (CNC/CAM) of milling machines. The student will apply this knowledge to design, simulate, build and program a robotic/mechatronic system. A central part of the course is to carry out experiments and writing an essay/article describing the robotic system that the student has created.

Course Leader: Mats Erling Høvin
Email: matsh@ifi.uio.no

ITLED4310 – Digitalisering - Universitetet i Oslo MA UiO 10 NA

Emnet gir en oversikt over sentrale problemstillinger, teorier og teknologier i forbindelse med digitalisering og digital transformasjon. Emnets kunnskapsgrunnlag er i hovedsak forskningsartikler om digitalisering og digital transformasjon, supplert med empirisk forskning om erfaringer med digitalisering i praksis. Hovedmålet er å gi deltakerne et bedre grunnlag for arbeid med digitaliseringsprosjekter - hva er mulighetene og hva er fallgruvene? Hva innebærer digitalisering for dine ansatte, kunder og for samfunnet forøvrig? Det legges vekt på å utvikle deltakernes evne til å være kritisk konstruktiv i forhold til begreper og teorier. Deltakernes egen erfaringsbakgrunn og jobbutfordringer vil bli trukket frem.

Course Leader: NA
Email: NA

IN4110 – Problemløsning med høynivå-språk - Universitetet i Oslo MA UiO 10 NA

Emnet gir en innføring i mer avanserte sider ved script- og programmeringsspråket Python, bl.a. objektorientert programmering, regulære uttrykk, interaksjon med operativsystemet, plattform-uavhengig kode, effektiv design av programsystemer med tidskritiske operasjoner, utvidelser i kompilerte språk som C/C++, data-analyse og web-programmering. Emnet gir også en grunnleggende innføring i script-språket Bash, testing og dokumentering av kode, og versjonskontrollsystem git. Spesiell vekt legges på praktisk problemløsning med et fokus på interessante og studierelevante oppgaver.

Course Leader: NA
Email: NA

MCT4047 – Music and Machine Learning - University of Oslo MA UiO 5 Stefano Fasciani

The aim of the course is to develop knowledge of and practical experience with machine learning algorithms applied in music analysis, music information retrieval, interactive music systems, and algorithmic music.

Course Leader: Stefano Fasciani
Email: stefano.fasciani@imv.uio.no

MCT4052 – Music and Machine Learning - University of Oslo MA UiO 10 NA

The aim of the course is to develop knowledge of and practical experience with machine learning algorithms applied to music analysis, music information retrieval, interactive music systems, and algorithmic music.

Course Leader: NA
Email: NA

ECON4170 – Data Science for Economists - University of Oslo MA UiO 10 Jo Thori Lind

The first part of the course is an introduction to programming and common programming structures. The course goes on to cover manipulation of data, data analysis including an introduction to machine learning techniques, and basic numerical methods useful in economics.

Course Leader: Jo Thori Lind
Email: j.t.lind@econ.uio.no

IN5550 – Neural Methods in Natural Language Processing - Universitetet i Oslo MA UiO 10 Erik Velldal

This course studies a selection of advanced techniques in Natural Language Processing (NLP), with particular emphasis on recent and current research literature. The focus will be on machine learning and specifically ‘deep’ neural network approaches to the automated analysis of natural language text. Topics will typically include representation learning for words (and possibly larger linguistic units), classification using Convolutional Neural Networks, and applications of various types of Recurrent Neural Networks to sequence labeling and the analysis of grammatical or semantic structure. The course includes strong practical components and puts emphasis on NLP problems and (potentially large) datasets of central importance in current research. Thus, students will be prepared to pursue an experimental, research-oriented MSc project in Natural Language Processing.

Course Leader: Erik Velldal
Email: erikve@ifi.uio.no

MA-447-G AI Mathematics MA UiA 7.5 NA

The course focuses on mathematical principles needed for practical machine learning tasks. This includes: 1) probability and information theory including random variables, chain rule of the conditional probabilities, and properties of mathematical functions commonly used on machine learning 2) topics from linear algebra essential for machine learning tasks 3) numerical computations including gradient descent and constrained optimization 4) core mathematical concepts in machine learning such as maximum likelyhood estimation, regression techniques, classification evaluation, and dimensional re-duction techniques 5) identifying and developing solutions for Mathematical Game theory 6) developing dynamical systems including but not limited to time dependent functions, deterministic and stochastic state space, and evolution rules 7) the theory and practice of Markov chains.

Course Leader: NA
Email: NA

STA-8002 Multivariable Statistical Analysis MA UiT 10 NA

The course gives a thorough introduction to the multivariate normal distribution, as well as estimation of its parameters. It further presents various areas in multivariable statistical analysis, such as the classification problem, testing of general linear hypotheses, principal component analysis, canonical correlation and factor analysis.

Course Leader: NA
Email: NA

INE-3601 - Robotics in manufacturing systems MA UiT 5 NA

Introduction to the industrial robot. Design of robots and their industrial applications. Technical, economic, and organizational issues related to the implementation of robotics in advanced flexible manufacturing systems

Course Leader: NA
Email: NA

STE6210 - Robotics in manufacturing systems MA UiT 5 NA

History and terminology Economical evaluation of flexible manufacturing systems Design of industrial robots Kinematic analysis of robotic arms Joint drive systems: electrical, hydraulic and pneumatic. Joint measurement systems: position and velocity measurement. Robot control architectures Robot programming methodologies External devices: camera systems, fixtures, feeders etc. Selected applications in manufacturing

Course Leader: NA
Email: NA

INF-6003 - Grunnleggende introduksjon til kunstig intelligens (KI) og maskinlæring (ML) MA UiT 0 NA

Emnet gir kursdeltakerne en grunnleggende forståelse for området kunstig intelligens (KI) inklusive maskinlæring (ML). Kursen gir kandidatene en forståelse av feltet kunstig intelligens og dens metoder og teknikker. Det betyr å lære en nøyaktig forståelse av begrepene som brukes i feltet og å kunne bruke dem riktig i en presentasjon eller dialog om emnet.

Course Leader: NA
Email: NA

DTE-3608 - Artificial intelligence and intelligent agents - introduction MA UiT 5 NA

Course content: 1) The students will be introduced to AI as a discipline and scientific research areas and understand its industrial and societal impact 2) The student will be introduced to an array of basic methods illustrating the different fields of AI and machine learning 3) A selection of decision and machine algorithms will be presented in depth and compared to each other 4) The subject of information management and decision theory with scarce or corrupted data will be treated 5) The course will also introduce agents, agent architectures and agent learning for individual as well as colonies and groups.

Course Leader: NA
Email: NA

DTE-3606 - Artificial intelligence and intelligent agents - project MA UiT 5 NA

The course content: 1) The students will have an introduction in time series and regressions supporting predictions based on historic data. ARMA techniques will be addressed 2) Preprocessing techniques like PCA will be practiced 3) Practical use of cluster techniques, Support Vector Machines and Classification and Regression trees (CART) will be exercised 4) The course will explore in depth a selection of neural networks such as LSTM and CNN 5) The students will be introduced to MAS systems and theoretical concepts associated with these i.e. ontologies, ACL, architectures, game theory and MAS-learning.

Course Leader: NA
Email: NA

STE6246-002 - Artificial intelligence and intelligent agents MA UiT 5 NA

Course content: 1) The students will have an introduction in time series and regressions supporting predictions based on historic data. ARMA techniques will be addressed 2) Preprocessing techniques like PCA will be practiced 3) Practical use of cluster techniques, Support Vector Machines and Classification and Regression trees (CART) will be exercised 4) The course will explore in depth a selection of neural networks such as LSTM and CNN 5) The students will be introduced to MAS systems and theoretical concepts associated with these i.e. ontologies, ACL, architectures, game theory and MAS-learning.

Course Leader: NA
Email: NA

INF367 - Selected Topics in Artificial Intelligence MA UiB 10 Information Not available

The course deals with current topics in artificial intelligence, and its content will vary from time to time the course is taught. Topic for autumn 2021: Ontologies and Knowledge Graphs

Course Leader: Information Not available
Email: studieveileder@ii.uib.no

INF367A - Selected Topics in Artificial Intelligence II MA UiB 10 NA

The course deals with current topics in artificial intelligence, and its content will vary from time to time the course is taught. Topic for spring semester 2021: "Machine learning and societal questions"

Course Leader: NA
Email: studieveileder@ii.uib.no

INF368 - Selected Topics in Machine Learning MA UiB 10 NA

The course deals with current topics in machine learning, and its content will vary from time to time the course is taught. Topic for spring semester 2021: "Reinforcement learning"

Course Leader: NA
Email: studieveileder@ii.uib.no

INFO381 - Research Topics in Artificial Intelligence MA UiB 15 Information Not available

The course covers advanced theoretical and technical issues in artificial intelligence. It will focus on some selected topics and applications, such as artificial life, agent and multi-agent systems, machine learning, neural networks, genetic algorithms and programming, data mining, natural language processing, case-based reasoning, cognitive science, and neuro-computing.

Course Leader: Information Not available
Email: studieveileder@ii.uib.no

ELMED219 - Artificial intelligence and computational medicine MA UiB 6 Arvid Lundervold

The objective and content of the course addresses The computational mindset, machine learning and AI in future medicine - pros et cons A guided tour of some mathematical and statistical modelling techniques in biomedical and clinical applications. Examples and demonstrations will be related to in vivo imaging and integrated quantitative physiology, imagingderived biomarkers, omics data, and sensor data. Operational principles of selected sensors and measurement devices in biomedical research and clinical practise - from smartphones to MRI scanners. The concepts of "big data", "data analytics", "machine learning", and "deep convolutional neural networks" with examples from personalized and predictive medicine. Throughout the course, the students will use principles and tools from numerical programming, data analysis, and scientific computing for medical applications. This will provide an introduction to e.g. R, Python, and Jupyter notebooks, and "the cloud" for data storage and computations. The concepts and importance of "open science", "data sharing", and "reproducible research".

Course Leader: Arvid Lundervold
Email: elektiv@med.uib.no

JUS295-2-A - Legal Technology: Artificial Intelligence and Law MA UiB 10 Associate Professor Knut Martin Tande

Legal Technology refers to the use of technology, software and computer analytics to provide legal services and justice. It is increasingly transforming legal practice and institutions and the nature of law and research. The most prominent development is the rise of computational applications in artificial intelligence in legal fields diverse as asylum, contracts, policing and finance. Moreover, creative uses of digital platforms and blockchain technology are providing new possibilities in dispute resolution, legal registries and private law orderings. This course will critically explore current trends and future possibilities of this transformation from the perspectives of legal science, computer science, social science and ethics. Students will: Learn about long-standing theory in law and artificial intelligence Study the rise of diverse computational law methods and processes Explore potential future applications and development Critically examine the sociology and ethics of this transformation for law and the legal profession. Meet leading legal technology actors

Course Leader: Associate Professor Knut Martin Tande
Email: elective-courses.jurfa@uib.no

ITØK320 - Supply Chain Analytics MA UiB 10 Information Not available

The course aims to give the students an overview of supply chain management, understanding of how to use advanced optimization techniques and artificial intelligence (AI) algorithms to solve and analyze decision problems, and ability to solve decision problems occurring in different segments of a supply chain, with a focus on the transportation and logistics industry. an overview of supply chain management, understanding of how to use advanced optimization techniques and artificial intelligence (AI) algorithms to solve and analyze decision problems, and ability to solve decision problems occurring in different segments of a supply chain, with a focus on the transportation and logistics industry.

Course Leader: Information Not available
Email: studieveileder@ii.uib.no

INFO382 - Logic for Multi-agent Systems MA UiB 15 Information Not available

The course covers the state of the art in logical formalisms for reasoning about multi-agent interaction. Formal logic is of key foundational and practical importance in the fields of multi-agent systems and artificial intelligence. The course will focus on the logical formalization of different types of interaction between rational agents. A main topic will be epistemic logic, logic for reasoning about knowledge and belief. Epistemic logic has had a very strong impact, not only in multi-agent systems and artificial intelligence, but also elsewhere in computer science, in philosophy, in game theory, and in other fields. In addition to epistemic logic, the course will cover logics for reasoning about time and game-like interaction, such as coalition logic and alternating-time temporal logic, as well as other types of multi-agent interaction, and the dynamics of combining several of these types of reasoning. The emphasis will be on formal models and logical languages and their formal semantics. Some proof techniques will be covered.

Course Leader: Information Not available
Email: studieveileder@ifi.uib.no

INFO323 - Data Architectures for Information Retrieval and Web Intelligence MA UiB 15 Information Not available

The course focuses on the information retrieval as goal and as a part of the process of extracting knowledge from the Web resources. It goes beyond the click-stream analysis and explores techniques for discovery of facts and new patterns. The course also provides insights into Web dynamic looking at challenges of constant change and how it affects Web content, size, topology and use.

Course Leader: Information Not available
Email: studieveileder@ifi.uib.no

MIX301 - Media Technology: Theory and Development MA UiB 15 Information Not available

The course is an introductory course to the Master's Programme in Media and Interaction Design. The aim of the course is to provide students with critical and creative knowledge of advanced media technology concepts and systems. The course focuses on a technological concept (for example visualization, artificial intelligence, or 3D modelling) that will serve as a case through the semester. The students will study and discuss technology philosophy that illuminates the chosen technology from different perspectives, and they will explore and experiment practically with the technology in question with the goal of applying it to new areas.

Course Leader: Information Not available
Email: studieveileder.praktiskinfomedia@uib.no

JUS294-2-A - Privacy and Data protection - GDPR MA UiB 10 Information Not available

The course studies legal rules on data protection, that is a set of norms that govern the processing of personal data with the view of protecting the privacy of individuals whose data is being processed. The EU General Data Protection Regulation 2016/679 (GDPR) defines personal data as any information relating to an identified or identifiable natural person such as a name, an identification number, location, an online identifier or any factor specific to the physical, physiological, genetic, mental, economic, cultural, or social identity of that natural person. Given the digital technologies' encroachment into our lives, the right to privacy and the protection of personal data have become crucial to both individuals, communities and businesses. A proper understanding of the rules governing data protection is now also necessary when working with other fields of law such as administrative law, EU/EEA competition law, public procurement, intellectual property law, health law or labour law.

Course Leader: Information Not available
Email: elective-courses.jurfa@uib.no

INFO352 - Research Topics in Technology Enhanced Learning MA UiB 15 Information Not available

The aim of the course is to give an understanding of central perspectives, theoretical foundations and concepts related to the use of advanced technology for learning and teaching. It provides in-depth, cross-disciplinary coverage of the field of technology enhanced learning that is at the cross roads of technology, education, psychology, and applied social science.

Course Leader: Information Not available
Email: studieveileder@ifi.uib.no

FIL335 - Master Topic in Philosophy of Mind MA UiB 10 Information Not available

After completing the course, the student should have - overview of the most important directions within the philosophy of mind in the 20th century, such as behaviorism, the psychophysical identity theory, functionalism, characteristic dualism and eliminative materialism - insight into the issues that are common challenges for these directions, such as the question of what intentionality is, the question of the experience of awareness relative to a person's material properties and processes, and how we can understand mental causation - insight into issues that connects philosophy of mind to modern cognitive science, important here is the question whether Artificial Intelligence can exist

Course Leader: Information Not available
Email: studierettleiar@fof.uib.no

JUS296-2-A - Law of armed Conflict, with emphasis on maritime operations MA UiB 10 Information Not available

The Law of Armed Conflict (LOAC) applies between parties to an armed conflict, be it an international armed conflict or non-international armed conflict (sometimes called an internal armed conflict). The subject of LOAC is therefore not when or if parties to a conflict have the right to resort to armed force - but the rules applicable between them if they do.

Course Leader: Information Not available
Email: elective-courses.jurfa@uib.no

INF241 - Quantum Information, Quantum Computing, and Quantum Cryptography MA UiB 10 Information Not available

The course will teach the subject of quantum information theory, with its application to quantum computation and quantum cryptography ( in the context of both quantum-secure primitives and quantum attack scenarios). Depending on time, there may also be a very brief discussion of the potential application of quantum computing in the context of machine learning.

Course Leader: Information Not available
Email: studieveileder@ii.uib.no

INFO383 - Research topics in AI Ethics MA UiB 15 Information Not available

AI ethics is the common reference to a collection of sub-fields in AI developed to respond to the issues of how to manage the moral, personal and societal impact of replacing people tasks and roles with AI powered computing. AI Ethics comprises of four main research sub-disciplines: fair-accountable-transparent AI (FAccT), explainable AI (XAI),responsible AI, and machine ethics (also called artificial morality). This course gives an introduction to AI Ethics and a general overview of the state of the art in AI ethics through immersing the students in the research process of an AI ethics topic.

Course Leader: Information Not available
Email: studieveileder@ifi.uib.no

INF272 - Non-Linear Optimization MA UiB 10 Information Not available

The course contains the basic framework for constructing efficient methods for solving unconstrained optimization problems. Topics include line search, trust regions and derivative-free methods for unconstrained optimization. For constrained optimization the Karush-Kuhn-Tucker theory and basic solution techniques are presented. The close connection to Machine Learning and stochastic gradient descent is discussed.

Course Leader: Information Not available
Email: studieveileder@ii.uib.no

INFO345 - Research Topics in Recommender Systems MA UiB 15 Information Not available

This course offers an overview of approaches to develop and evaluate state-of-the-art recommender system methods. In particular, this course makes an extensive introduction to current algorithmic approaches for generating personalized recommender approaches, such as collaborative and content-based filtering, as well as more advanced methods such as hybrid recommender approaches, context-aware methods and approaches relying on machine learning techniques. The course will also discuss in detail how to evaluate recommender systems from an algorithmic and an interface perspective and what needs to be considered when adopting standard recommender approaches to particular domains or use cases.

Course Leader: Information Not available
Email: Studierettleiar@ifi.uib.no

LING310 - Computer Models of Language and Applications MA UiB 15 Information Not available

The content of the course may vary with the lecturer, but will focus on theories and methods for the development of linguistic models and applications. Examples of relevant topics in modeling are classification algorithms based on rule induction, neural networks, nearest neighbor, etc. Examples of language technology applications are interfaces between human and machine, machine translation, proofreading, information retrieval, and information technology aids for persons with disabilities. This course is also suitable for students in e.g. information science and digital humanities.

Course Leader: Information Not available
Email: advice@lle.uib.no

ECON327 - Game Theory MA UiB 10 Information Not available

This course will cover the models and tools from the field of game theory, and will introduce students to a host of applications of game theory. The goal will be to provide students with an advanced level of knowledge and understanding of the modeling of strategic situations. To enhance students' understanding of the different theories, special attention will be paid to teaching relevant applications.

Course Leader: Information Not available
Email: studieveileder@econ.uib.no

DAT300 - Anvendt maskinlæring II MA NMBU 10 Oliver Tomic

DAT300 bygger videre på temaer som ble diskutert i DAT200 - Anvendt maskinlæring. 1) Strategier for egenskapsgenerering (feature engineering) for maskinlæring 2) Grunnlag for kunstige nevrale nett (NN) 3) Deep convolutional neural networks (CNN) 4) Recurrent neural networks (RNN) 5) Prosessering og analyse av store datasett. Kurset gir en innføring i metodenes grunnleggende teoretiske egenskaper, men har hovedfokus på anvendt modellering med reelle datasett.

Course Leader: Oliver Tomic
Email: oliver.tomic@nmbu.no

INN355 - Maskinlæring for optimalisering av forretningsprosesser MA NMBU 10 Joachim Scholderer

Kurset har to mål. Første målet er å introdusere deltakere til maskinlæringsmetodikkene og datavitenskapsverktøyene som er sentrale i forretningsapplikasjoner, det andre er å dyrke deltakernes evne til å håndtere maskinlæringsprosjekter i ekte forretningskontekster. En rekke eksempler kan lese om en følger link til emnesiden.

Course Leader: Joachim Scholderer
Email: joachim.scholderer@nmbu.no

GMPE340 - Kalmanfilter og sensorintegrasjon MA NMBU 5 Jon Glenn Omholt Gjevestad

Introduksjon til stokastiske prosesser og anvendt Kalmanfiltrering med fokus på posisjonerings-, navigasjons- og tidsanvendelser (PVT).

Course Leader: Jon Glenn Omholt Gjevestad
Email: jon.glenn.gjevestad@nmbu.no

ACIT4510 - Statistical Learning MA OsloMet 10 Pedro Lind

The class covers foundations and recent advances of Machine Learning from the point of view of Statistical Learning Theory. The goal of this class is to provide students with the practical skillset to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Course Leader: Pedro Lind
Email: Pedro.Lind@oslomet.no

ACIT4610 - Evolutionary artificial intelligence and robotics MA OsloMet 10 Stefano Nichele

This course will present complex systems (cellular automata, networks, and agent-based) modelling and programming through state-of-the-art artificial intelligence methods that take inspiration from biology (sub-symbolic and bio-inspired AI methods), such as evolutionary algorithms, neuro-evolution, artificial development, swarm intelligence, evolutionary and swarm robotics.

Course Leader: Stefano Nichele
Email: stefano.nichele@oslomet.no

ACIT4040 - Applied Artificial Intelligence Project MA OsloMet 10 Stefano Nichele

A real artificial intelligence project will be carried by a large team of students. A practical application will be targeted using state-of-the-art methods and tools. The students will construct a working system from scratch, implementing machine learning components as well as using existing tools. The students are involved in the entire process, starting from earlier design choices to the AI system completion. Examples of tasks may include speech processing and image recognition, robots or drones navigation, self-driving vehicles, chatbots, etc.

Course Leader: Stefano Nichele
Email: stefano.nichele@oslomet.no

ACIT4620 - Computational Intelligence: Theory and Applications MA OsloMet 10 Jianhua Zhang

This course will cover fundamentals of computational intelligence (CI) techniques - modern approaches to artificial intelligence (AI), as well as several advanced topics such as adaptive-network-based fuzzy inference systems (ANFIS) and neuro-evolution. The main topics include definitions of AI and CI, history of AI and CI, symbolic vs. connectionist AI methods, mainstream CI approaches (artificial neural networks, fuzzy systems and evolutionary computation), and some representative applications of CI.

Course Leader: Jianhua Zhang
Email: Jianhua.Zhang@oslomet.no

ACIT4630 - Advanced Machine Learning and Deep Learning MA OsloMet 10 Raju Shresta

This course provides a broad introduction to machine learning (ML), which includes supervised, unsupervised, and reinforcement learning, and deep learning (DL) that can be used in different application domains. Students will learn both theories and practices in ML and DL. Moreover, students will learn from studying, presenting, and discussing relevant research articles and expose themselves to research by doing a research project.

Course Leader: Raju Shresta
Email: aju.Shrestha@oslomet.no

ACIT4030 - Machine Learning for images and 3D data MA OsloMet 10 Henrik Lieng

This course will present the state of the art in algorithms for machine learning on images and 3D data. After a brief introduction to image processing and 3D geometry, we will cover topics within both supervised and unsupervised learning. The course covers classical problems like classification, segmentation, and correspondence detection. Recent work on shape and image synthesis will also be discussed. We will in particular study deep neural architectures for 2D images and 3D data such as point clouds and shape graphs. Additionally, 3D shape design with generative models will be presented.

Course Leader: Henrik Lieng
Email: Henrik.Lieng@oslomet.no

ACIT4820 - Applied Robotics and Autonomous Systems MA OsloMet 10 Alex Alcocer

This course provides a hands-on overview of common theories and methods used in the design of robotic and autonomous systems. The course is organized around weekly practical labs and lectures that complement each other. The student will get hands-on experience with the technologies, algorithms, and architecture of robotic and autonomous systems. The course uses examples from aerial, space, ground, underwater, and industrial robotic and autonomous systems.

Course Leader: Alex Alcocer
Email: alex.alcocer@oslomet.no

ACIT4020 - Aerial Robotics MA OsloMet 10 NA

Unmanned Aerial Vehicles (UAVs) are a disruptive technology that is revolutionizing data gathering, earth observation, environmental monitoring, mapping, and transport to name only a few. This course provides a hands-on overview of common theories and methods used in the design of aerial robotic systems. The course is organised around weekly practical labs and lectures that complement each other. The student will get hands-on experience with the technologies as well as a holistic perspective on the architecture of aerial robotic systems. The course uses examples from multirotor and fixed wing types of vehicles and focuses both on autonomous and remotely piloted aerial systems (RPAS).

Course Leader: NA
Email: NA

ACIT4830 - Special Robotics and Control Subject MA OsloMet 10 Evi Zouganeli

The course provides an arena where students can learn about specific technologies and methods that are relevant for applications in robotics and control. These themes can be varied from artificial intelligence methods for robotics and control, Internet of Things and sensor network systems, autonomous and distributed systems, embedded systems, industrial process control, and other special subjects within robotics and control.

Course Leader: Evi Zouganeli
Email: Evi.Zouganeli@oslomet.no

ACIT4810 - Advanced Methods in Modelling, Simulation, and Control MA OsloMet 10 Tiina Komulainen

The course covers several aspects of model-based control and estimation methods. The focus is on industrial applications, implementation, real life problems, and hands-on experience. The course gives an overview of state-of-the-art techniques, and provides students with tools to analyse and solve further industrial and research problems. Strong emphasis is given to the use of numerical simulation and scientific programming with Matlab/Simulink or similar.

Course Leader: Tiina Komulainen
Email: tiina.komulainen@oslomet.no

ACIT4080 - Intelligent User Interfaces MA OsloMet 10 Eika Sandnes

The course focuses on the application of artificial intelligence in user interfaces, techniques, technologies and ethical consequences.

Course Leader: Eika Sandnes
Email: Frode-Eika.Sandnes@oslomet.no

ELE130 - Anvendt matematikk og fysikk i robotprogrammering MA UiS 10 Tormod Drengstig

Anvende matematikk- og fysikkunnskaper til å løse ulike problemstillinger i robotprogrammering. Forstå og kunne forklare begrepene numerisk integrasjon, filtrering og numerisk derivasjon, samt kunne implementere og bruke disse numeriske metodene i MATLAB og Python. Utvikle, implementere og simulere ODE-baserte modeller av dynamiske system ved å bruke balanselover innenfor kinematikk, fluiddynamikk og termodynamikk (impuls-, masse- og energibalanser). Få en innføring i utvalgte tema innen kinematikk, termodynamikk, fluiddynamikk, elektrisitetslære, bølgefysikk og elektromagnetisme.

Course Leader: Tormod Drengstig
Email: tormod.drengstig@uis.no

DAT550 - Datautvinning og dyplæring MA UiS 10 Vinay Jayarama Setty

Formålet med dette kurset er at studentene skal få kunnskap og praktisk erfaring med datautvinning (data mining) og dyplæringsteknikker. Emnet skal gi studentene dyp kjennskap til teknologier for datautvinning. Studentene skal kunne forberede storskala data for datautvinning (forbehandling), featureekstraksjon, dimensjonsreduksjon og bruke en rekke datautvinningsmetoder og dyplæring for klassifisering, regresjon og klynging oppgaver some hjelper for å trekke ut nyttig kunnskap fra data. Kurset skal gi studentene mulighet til å lære moderne datautvinnings- og dyplæringsalgoritmer og -verktøy. Studentene vil få praktisk erfaring gjennom å prøve disse verktøyene på ekte data.

Course Leader: Vinay Jayarama Setty
Email: vinay.j.setty@uis.no

ELE680 - Dype nevrale nett MA UiS 5 Øyvind Meinich-Bache

I dette emnet vil du bli introdusert til grunnleggende metoder for dyplæring, de mest effektive og vanlige typer dype nettverk som benyttes og hvordan du kan bygge, trene og evaluere dype nett for ulike applikasjoner.

Course Leader: Øyvind Meinich-Bache
Email: oyvind.meinich-bache@uis.no

ELE510 - Bildebehandling og maskinsyn MA UiS 10 Kjersti Engan

Bidrag fra både tradisjonell bildebehandling og datasyn (computer vision) brukes til å konstruere systemer for robotsyn/maskinsyn. Det er en hurtig utvikling innen dette området og anvendelser finnes både i industrien og innen forskning. Det finnes mange produkter som inneholder kamera og programvare for behandling av visuelle data.

Course Leader: Kjersti Engan
Email: kjersti.engan@uis.no

ELE520 - Maskinlæring MA UiS 10 Trygve Christian Eftestøl

Kurset fokuserer på metoder for læring av underliggende strukturer representert i data og trening av modeller son kan gjøre prediksjoner på nye data. Slike prediksjoner kan typisk være å skille mellom ulike kategorier av data, det vil si klassifisering, som vil være hovedfokus for dette kurset.

Course Leader: Trygve Christian Eftestøl
Email: trygve.eftestol@uis.no

ELE610 - Praktisk robotteknikk MA UiS 10 Karl Skretting

Emnet er delt i to likeverdige deler: Bildefangst med bildebehandling i Python og RobotStudio for styring av ABB-roboter.

Course Leader: Karl Skretting
Email: karl.skretting@uis.no

ELE600 - Videregående reguleringsteknikk med robotteknologi MA UiS 10 Kristian Thorsen

Kurset omhandler tilbakekoblede systemer, stabilitetsanalyse, regulatorinnstilling, tabelloppslag (gain scheduling), kaskaderegulering, foroverkopling, dødtidskompensering og multivariabel regulering. Robotteknologi-delen omhandler grunnleggende robotteknologi med koordinatsystem, Denavit-Hartenberg konvensjonen, forover- og bakover kinematikk, posisjons- og hastighetsregulering av leddene i roboten.

Course Leader: Kristian Thorsen
Email: kristian.thorsen@uis.no

CS4020 - Data Science MA USN 10 NA

Address the latest issues in Data Centric Computer Science in terms of: 1) Teaching students how to manage and interpret the semantic of data, define its role and perform and evaluate data analysis in modern computing 2) Raising awareness of the changes in data management, triggered by advances in technologies and computational power 3) Investigating a range of applications of modern data management, with emphasis on data processing with AI and the use of ML algorithms. 4) Enabling students to run debates on and understanding of the nature of the intersection of computer science, statistics and learning technologies. 5) Giving students access to the latest trends in developing data centric computing and helping them to build critical assessment of software technologies and computational models prevalent in our times

Course Leader: NA
Email: NA

IIA1420 Machine Learning and Sensor Technology MA USN 10 NA

The course provide an introduction to machine learning, emphasizing applications within the field of sensor technology.

Course Leader: NA
Email: NA

ITI41720 Machine Learning MA HiØ 10 Roland Olsson

This course gives an advanced insight into the main methods used in machine learning. The topics covered in this course are: 1) Concepts related to basic types of learning (supervised, unsupervised, reinforcement): preprocessing, feature extraction, overfitting, error functions 2) Decision and regression trees, random forest and XGBoost 3) Artificial neural networks, deep learning 4) Optimization (evolutionary algorithms and other search methods) 5) Bayesian inference / classification 6) Ethics and privacy in machine learning is also mentioned.

Course Leader: Roland Olsson
Email: roland.olsson@hiof.no

ITI41820 Advanced Topics in Machine Learning MA HiØ 10 NA

The course goes in depth on selected topics and methods within machine learning and their applications. Examples include: 1) advanced neural net and deep learning models, such as: ResNET, Zero shot, GAN, LSTM 2) Evolutionary and bio-inspired algorithms algorithms (like GA, EA, ES, PSO, ACO, AIS) in search, optimization and classification. 3) Program induction. Symbolic regression. Automatic programming. 4) Markov models, Kernel methods. SVM 5) Implementing machine learning in Industries and business 6) Machine learning challenges and future 7) Philosophical fundamental problems and ethical questions related to machine learning.

Course Leader: NA
Email: NA

IE502014 - Artificial Intelligence MA NTNU 7.5 Hans Georg Schaathun

This course gives an introduction to a number of selected topics in artificial intelligence (AI) relevant for solving real-world problems. The course will study AI with respect to modelling a variety of problems in suitable state space; design and implementation of intelligent search and optimization algorithms; simulation and testing of models and algorithms; and visualisation, interpretation, and analysis of the results.

Course Leader: Hans Georg Schaathun
Email: hasc@ntnu.no

IT3105 - Artificial Intelligence Programming MA NTNU 7.5 Keith Linn Downing

The course gives students the opportunity to implement many classic AI algorithms and use them as modules in large AI systems to perform tasks such as speech and image processing, simulated soccer (in the well-known Robocup on-line competition), Texas Hold'Em poker playing, and robot navigation.

Course Leader: Keith Linn Downing
Email: keithd@ntnu.no

TDT4173 - Maskinlæring MA NTNU 7.5 Kerstin Bach

The course deals with principles and methods for how computer systems themselves can update their knowledge and problem-solving ability. Classic methods for machine learning based on observed data, as well as learning that also utilizes existing knowledge are reviewed and analyzed.

Course Leader: Kerstin Bach
Email: kerstin.bach@ntnu.no

IE501714 - Swarm intelligence MA NTNU 7.5 Saleh Abdel-Afou Alaliyat

This course is designed to present an overview of Swarm Intelligence (SI) topic, including both behavioral swarm Intelligence and computational swarm intelligence, and applications of SI. The students will learn different swarm intelligence algorithms that are inspired by natural systems such as ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence.

Course Leader: Saleh Abdel-Afou Alaliyat
Email: alaliyat.a.saleh@ntnu.no

IMT6171 - Real-time AI for robotics and simulated environments MA NTNU 7.5 Sule Yildirim-Yayilgan

The candidate has the ability to evaluate and critique mechanisms for real time problem solving for various domains using robotics and simulations.

Course Leader: Sule Yildirim-Yayilgan
Email: sule.yildirim@ntnu.no

TTT4185 - Machine Learning for Signal Processing MA NTNU 7.5 Giampiero Salvi

Basic methods for statistical pattern recognition/machine learning. Deep neural networks, support vector machines, random forests, hidden Markov models, Gaussian processes. Design, training and evaluation of machine learning models. Extraction of feature vectors with applications to speech technology, medical signal processing and multimedia signal processing.

Course Leader: Giampiero Salvi
Email: giampiero.salvi@ntnu.no

TDT4173 - Machine Learning MA NTNU 7.5 Kerstin Bach

The course gives an introduction to the principles and methods for automatic learning in computer systems. Classical syntax-based learning methods as well as more knowledge-intensive methods are described...

Course Leader: Kerstin Bach
Email: kerstin.bach@ntnu.no

IP505314 - Best Practice - Machine Learning for Ship Autonomy MA NTNU 3.8 Guoyuan Li

In this course, we will introduce variant of machine learning methods and apply them for ship autonomy applications. The aim is to show the potential use of these methods for solving specific problems on autonomous ships, such as path planning, auto-docking and motion prediction.

Course Leader: Guoyuan Li
Email: guoyuan.li@ntnu.no

IE500618 - Machine Learning MA NTNU 7.5 Ibrahim Abdelfatta Abdelhameed Ibrahim

This course assumes that you know close to nothing about Machine Learning (ML). Its goal is to give you the concept, the intuitions, and the tools you need to implement programs capable of learning from data.

Course Leader: Ibrahim Abdelfatta Abdelhameed Ibrahim
Email: ibib@ntnu.no

TTK4260 - Multivariate analysis and Machine learning methods MA NTNU 7.5 Damiano Varagnolo

The course will have two distinct "working modes": one where the theory of the algorithms will be presented in detail, and one where the algorithms will be introduced and demonstrated without deriving them in detail. The first part deals with tools that are within the core knowledge that control engineers shall have. The second deals with ancillary tools and provides an overview of the possibilities offered by the current state-of-the-art methods within Machine Learning.

Course Leader: Damiano Varagnolo
Email: damiano.varagnolo@ntnu.no

KJ6020 - Experimental Design, Modelling and Machine Learning MA NTNU 7.5 NA

This course is an introduction to experimental design and machine learning methods for modeling and data analysis with emphasis on applications to chemistry, biotechnology, process chemistry, material science, and physics. The goal of the course is to provide knowledge on methods that can be used to extract useful information from complex data sets or physical processes.

Course Leader: NA
Email: NA

TDT4265 - Computer Vision and Deep Learning MA NTNU 7.5 Frank Lindseth

The content of the course ranges from the classical feature extraction and classification approach of vision to the more modern machine / deep learning based way of making sense of images and video. The course also contains a short summary of the programming skills and mathematical background needed as well as a recap of basic image processing & analysis methods in order to make sure that everybody is on the same page.

Course Leader: Frank Lindseth
Email: frankl@ntnu.no

IT3030 - Deep Learning MA NTNU 7.5 Helge Langseth

The course is a follow-up to TDT4173 Machine Learning. It gives thorough coverage of deep learning. The course covers both mathematical and computational foundation for deep learning, practical applications such as processing of images, text, and other modalities. Modern software frameworks for deep learning will be introduced and used for some projects, while other projects will require relatively low-level coding in Python or similar languages.

Course Leader: Helge Langseth
Email: helge.langseth@ntnu.no

IMT4392 - Deep learning for visual computing MA NTNU 7.5 Hao Wang

Introduction to deep learning (DL) - Deep neural networks (DNN) - Convolutional neural network (CNN) - Recurrent neural network (RNN) - Introduction to visual computing - Still-image and video processing - Enhancement, filtering and segmentation - Selected case studies on DL for visual computing

Course Leader: Hao Wang
Email: hawa@ntnu.no

TTK4195 - Modeling and Control of Robots MA NTNU 7.5 Anton Shiryaev

An overview over different types of manipulators: Kinematics, Dynamics, Motion Planning, Control. The course gives the foundation for developing robotic systems and designing manipulators. It provides comprehensive discussion of problems of service robotics and tasks encountered in outdoor environment. Applications are industrial robots, remotely operated manipulators for space and under water operations, service robots in unstructured environment.

Course Leader: Anton Shiryaev
Email: anton.shiriaev@ntnu.no

TTK4255 - Robotic Vision MA NTNU 7.5 Annette Stahl

At the end of the semester a successful student should have skills in processing and analysis of digital images and be able to design simple robot vision and machine vision systems. General competence: Be able to apply the fundamental imaging principles. Consciousness about the role of visual sensing in robotic applications. Be able to analyze strength and weaknesses of different vision based approaches.

Course Leader: Annette Stahl
Email: annette.stahl@ntnu.no

TPK4170 - Robotics MA NTNU 7.5 Lars Tingelstad

This course gives an introduction to robotics so that the student will have the competence to design and implement robotic systems. This is done by presenting topics from geometry, kinematics, dynamics, and control systems:...

Course Leader: Lars Tingelstad
Email: lars.tingelstad@ntnu.no

IP501508 - Robotics MA NTNU 7.5 Houxiang Zhang

The learning objective is to provide candidates with a sound understanding of selected issues within the field of Mechatronics: Robots and crane design, Kinematics and inverse kinematics, Dynamics and forces, Transmissions and actuators, Hydraulic servo systemsSensors - Controllers - Modelling and real time simulation

Course Leader: Houxiang Zhang
Email: hozh@ntnu.no

TPK4560 - Robotics and Automation, Specialization Project MA NTNU 15 Olav Egeland

Industrial robotics with a focus on applications within production. This includes kinematics and programming of industrial robots, robotic welding, robotic assembly, and other industrial applications. Computer vision for use in robotic systems is an important area.

Course Leader: Olav Egeland
Email: olav.egeland@ntnu.no

TPK4171 - Advanced Industrial Robotics MA NTNU 7.5 Olav Egeland

Kinematics: Screw theory. Projective geometry in 2D and 3D. Points, lines and planes in Plücker coordinates. Determination of rotations in SO(3) and displacements in SE(3) from measurements by using optimization methods. Robot vision: Camera models and calibration. RANSAC. Homographies in 2D and 3D: Properties and computation. Stereo vision with calibrated and uncalibrated cameras. Auto calibration. Point clouds and iterative closest point.

Course Leader: Olav Egeland
Email: olav.egeland@ntnu.no