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MA PhDLast updated: 12.07.2021
| Course | Level | University | ECTS | Leader | Description |
|---|---|---|---|---|---|
| IN9495 – Advanced Topics in Artificial Intelligence for Intelligent Systems | PhD | UiO | 5 | Jim Tørresen |
The course goes in depth on selected topics and methods within artificial intelligence (AI), machine learning (ML) and their applications. Examples include computational intelligence algorithms in search, optimization and classification, which to a large extent consist of bio-inspired mechanisms. |
| IN9550 – Neural Methods in Natural Language Processing | PhD | UiO | 10 | Stephan Oepen |
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. |
| IN9520 – Digital Image Analysis | PhD | UiO | 10 | Fritz Albregtsen |
The course covers methods for analysis of digital images, segmentation, and object description. Central topics are feature extraction and classification of objects in images. |
| TEK9010 – Multi-Agent Systems | PhD | UiO | 10 | Hans Jonas Fossum Moen |
This course gives you an introduction to systems with multiple agents/units/robots that mutually depend on each other’s behaviors in order to evaluate own or collective system performance. |
| TEK9030 – Computer Vision | PhD | UiO | 10 | Idar Dyrdal |
Computer vision is the study of how a machine, such as an unmanned system, can interpret and understand its surrounding environment using visual data such as images and video. |
| TEK9040 – Deep Learning for Autonomous Systems | PhD | UiO | 10 | Narada Dilp Warakagoda |
The course focuses on advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep learning techniques can be used to design important parts of advanced autonomous systems that exist in physical and cyber environments. |
| MF9150 – Essentials of Neurophysiology: from neurons to circuits to behaviours | PhD | UiO | 5 | Joel Glover |
For students interested in the nervous system, this course in neurophysiology covers the basic principles of neuron signalling and interactions that underlie brain function, spanning from the function of individual neurons to the function of neuronal circuits that produce behaviour. [...] The course is also suitable for students from the fields of chemistry, biology, immunology, pharmacology, psychology, informatics and biotechnology. |
| MF9155 – Introduction to statistics and bioinformatics for the analysis of large-scale biological data | PhD | UiO | 5 | Manuela Zucknick |
The course considers methods integral to data analysis in modern molecular medical research. It is planned that this course will be part 1 of a series of two courses on this topic. As such it is relevant to all PhD students and researchers who need to analyze large-scale molecular data themselves, as well as those who need to interpret results and understand publications in the molecular life sciences. |
| IN-STK9000 – Adaptive metoder for data-baserte beslutninger | PhD | 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. |
| STK-IN9300 – Statistical Learning Methods in Data Science | PhD | UiO | 10 | Riccardo De Bin |
Starting from the basic methods, the course will then cover more advanced procedures, specifically designed to tackle modern data challenges such as increasing complexity and large amounts of information (Big Data settings). |
| STK9011 – Statistical Inference Theory | PhD | UiO | 10 | Johan Pensar |
The course expands and is a thorough treatment of the theory of statistical inference introduced in earlier courses. The focus is treatment of point estimation and testing of hypotheses. |
| STK9051 – Computational Statistics | PhD | UiO | 10 | Geir Olve Storvik |
Statistical analysis is becoming more and more complex, both because of bigger data and many types of data and because of the use of more advanced methods and models. This course deals with numerical methods for performing such analysis, both traditional and more modern methods. |
| STK9060 – Time Series | PhD | UiO | 10 | Ingrid Hobæk Haff |
Estimation and testing of hypothesis with autoregressive processes and moving averages (i.e. ARMA-processes) and with stationary processes. Correlogram, periodogram, spectrum. State-space models (Kalman filter). Illustration on real data. |
| STK9190 – Bayesian nonparametrics | PhD | UiO | 10 | Nils Lid Hjort |
Statistical analysis involves first setting up a model for data in terms of certain unknown parameters. Bayesian analysis proceeds by placing a prior distribution on these parameters and then deriving and using relevant aspects of the consequent posterior distribution. |
| STK9200 – Advanced Statistical Methods | PhD | UiO | 10 | Geir Olve Storvik |
The precise contents of this course will vary, but will consist of selected themes of contemporary research in advanced statistical methodology. |
| INF9825 – Algorithms for Artificial Intelligence and Natural Language Processing | PhD | UiO | 10 | Stephan Oepen |
Foundational theory, with implementation in Common Lisp, concerning general techniques for searching, pattern matching, unification, knowledge representation, parsing and memoisation, with particular weight given to algorithms and data structures for the analysis of natural languages. |
| IN9400 – Maskinlæring for bildeanalyse | PhD | UiO | 10 | Anne H Schistad Solberg |
Emnet gir en innføring i teorien bak sentrale maskinlæringsalgoritmer som brukes i bildeanalyse. Videre beskrives utvalgte metoder og verktøy for dyp læring. |
| IN9490 – Advanced Topics in Artificial Intelligence for Intelligent Systems | PhD | 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. |
| IN-STK9100 – Reinforcement Learning and Decision Making Under Uncertainty | PhD | 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. |
| IN9030 – Protocols and Routing in the Internet | PhD | UiO | 10 | NA |
In this course you will meet distinguished speakers from industry and universities to give lectures on the topics listed below. Knowledge about the Wi-Fi technology, IP-router architecture, Big Data and Cloud solutions, Software Defined Networking (SDN), Multicast communication, Quality of Service, Basic optical communication, Internet of Things, Protocols for multimedia applications, Cyber security, and use of Artificial Intelligence (AI) and Machine Learning (ML) to deal with traffic control. |
| IN9410 – Energy Informatics - Universitetet i Oslo | PhD | UiO | 10 | Frank Eliassen |
The course provides an introduction to how informatics methods, techniques and tools can contribute to creating the sustainable energy systems of the future. Topics covered include machine learning, cloud computing, fog computing, Blockchain, data center, game theory and optimization and their application in different kinds of energy systems such as smartgrids with integrated solar and wind power, energy storage and electric vehicles. |
| IKT623 Principles of Artificial Intelligence | PhD | UiA | 5 | Ole-Christoffer Granmo |
The course will provide insight into the theory, foundations, implementation and applications of Artificial Intelligence (AI). It will bestow the students with the ability to use AI-methodologies in any application domain. |
| IKT724: Deep Learning | PhD | UiA | 5 | Baltasar Enrique Beferull Lozano |
This course offers an in-depth study on the mathematical and algorithmic foundations of deep neural networks (DNNs). |
| IKT710 Learning in Random Environments | PhD | UiA | 5 | Ole-Christoffer Granmo |
The heart of the course will involve deterministic and stochastic learning automata with fixed and variable structures. We will study their operation in random environments and the various norms of learning. The learning algorithms studied will be the linear and non-linear learning schemes of the continuous and discretised families with ergodic and non-ergodic properties. Estimator algorithms will also be examined. |
| IKT711 Principles of Pattern Recognition | PhD | UiA | 5 | Ole-Christoffer Granmo |
This course will introduce students to the principles of statistical and syntactic pattern recognition. After a brief review of the principles of probability, random variables and vectors, we will study Bayes decision theory and criteria for classification. We will then consider the theory of maximum likelihood and Bayesian learning for parametric pattern recognition. |
| IKT719 Advanced Optimization | PhD | UiA | 5 | Baltasar Enrique Beferull Lozano |
The theory and algorithms will be interlaced with several applications in different disciplines: selected applications in areas such as signal processing, data analytics, big data, machine learning, control, circuit design, wireless communication & sensor networks, distributed processing on graphs. |
| IKT720 Optimization | PhD | UiA | 5 | Baltasar Enrique Beferull Lozano |
The various optimization techniques will be continuously illustrated to solve important engineering problems in different areas, such as approximation and fitting, statistical signal processing, classification, problems on graphs and communication networks, control, computational geometry, data analytics, machine learning, task scheduling and portfolio optimization. |
| IKT722: Introduction to mmWave Sensing | PhD | UiA | 5 | Linga Reddy Cenkeramaddi |
The goal of this course is to give the students a basic knowledge on mmWave Sensors (FMCW Radars) which are important parts of contemporary electronics (for example: design and development of advanced driver assistance systems (ADAS)). |
| IKT723: Wireless Prototyping using SDRs (Software Defined Radios) | PhD | UiA | 5 | Linga Reddy Cenkeramaddi |
Students begin with building a VI, data transfer and communication. Advanced topics like programming in Parallel, FPGA Programming with LabVIEW, Multirate diagrams and FPGA will be introduced after covering basics.Using optimized FPGA VIs, designing with Clock-Driven logic and implementation of machine learning algorithms will also be covered in this course. |
| MAS601 Design, Modelling and Simulation of Mechatronic Systems | PhD | UiA | 5 | Michael Rygaard Hansen |
The learning outcome of the modelling and simulation part of the course is an overview of the state-of-the-art within modelling of mechatronic systems. The successful candidate will know the governing equations for steady-state and dynamic modelling of the basic electronic, electrical, hydraulic, pneumatic and mechanical sub-systems of a mechatronic system. |
| MAS602 Advanced Control and Robotics | PhD | UiA | 5 | Michael Ruderman |
The learning outcome of the course is insight into current research topics within advanced control systems and robotics. The successful candidate will have knowledge of the state-of-the-art within a number of topics related to analysis and design of multivariable and nonlinear systems. |
| IKT901-G Introduksjon til maskinlæring | PhD | UiA | 5 | NA |
Innhold: 1) Supervised learning: decision trees, kunstige neural nett, Bayesian læring
2) Unsupervised learning: K-means clustering, hierarchical clustering, principal components
3) Introduksjon til reinforcement læring 4) Real-world anvendelse av maskinlæring (f.eks selvkjørende biler, medisinsk bildeprosessering, tale/språkprosessering, e.l.) |
| FYS-8012 Pattern Recognition | PhD | UiT | 10 | Stian Normann Anfinsen |
The course covers data analysis techniques such as Bayes classifiers, linear classifiers, multilayer perceptrons, linear and nonlinear support vector machines, feature extraction techniques such as principal component analysis (ordinary and kernel version), Fisher discriminant analysis and Laplacian eigenmaps, and clustering algorithms of the sequential, hierarchical and function-optimising type, including hard and fuzzy k-means, Gaussian mixture modelling, and spectral clustering. |
| FYS-8033 Deep Learning | PhD | UiT | 10 | Michael Kampffmeyer |
This course will study recent deep learning methodology such as e.g. convolutional neural networks, autoencoders and recurrent neural networks and provide the students with the required background and up-to-date knowledge to implement, train and debug these models. The students will gain hands-on experience on contemporary problems in image processing, pattern recognition and statistics. |
| FYS-8032 Health Data Analytics | PhD | UiT | 10 | Michael Kampffmeyer |
NA |
| FYS-8024 Biomedical Instrumentation and Imaging | PhD | UiT | 10 | Svein Ketil Jacobsen |
The course will examine various imaging techniques including X-Ray, ultrasound, nuclear, MRI, microwave, and optical techniques. Emphasis will be put on the underlying physics and the technical mechanisms for image generation. It will be shown how images are formed and how various types of information are extracted. |
| INF-8207 Advanced mHealth Systems and Applications | PhD | UiT | 10 | NA |
This course covers advanced principles of mHealth (mobile health) systems and applications. The course addresses classic principles for design and implementation of mHealth systems and applications and discusses emerging mHealth trends from the international research front. |
| INF-8710 Multimedia Information Retrieval | PhD | UiT | 10 | Dag Johansen |
The course covers content-based operations such as indexing, retrieval, filtering, summarization, and information extraction and is applied to text, image, spoken audio and digital video. Particular importance will be allocated to the problem of testing and evaluation of information retrieval systems. |
| STA-8001 Computer-intensive statistics | PhD | UiT | 10 | Georg Elvebakk |
The course includes stochastic simulation, bootstrapping, Bayes theory, Laplace methods, the EM algorithm and Markov chain Monte Carlo (MCMC) techniques. |
| INFO901 - Introduction to AI Ethics | PhD | UiB | 10 | Marija Slavkovik |
The course introduces the fast evolving interdisciplinary research area of Artificial Intelligence (AI) Ethics to doctoral students who are interested in either AI as a computer science discipline or students interested in researching the societal and personal impact of AI technologies introduced in society. |
| INF241 - Quantum Information, Quantum Computing, and Quantum Cryptography | PhD | 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. |
| FYS488 Computational Neuroscience | PhD | NMBU | NA | NA |
Selected topics related to mathematical modelling of (i) signal processing in nerve cells, (ii) neural coding and decoding, (iii) receptive fields in the visual system, (iv) information transmission in the nervous system, (v) biophysics of nerve cells, (vi) biological neural networks, and (vii) learning and memory. |
| BIN420 Bioinformatics for functional meta-omics | PhD | NMBU | NA | NA |
This course will introduce, explore and assess the vast array of sequencing technology and bioinformatic methods that are available to address these core issues. The course will include an array of contrasting tools to decrypt microbial communities, including those that assess community structure (metagenomics, predictive genome-reconstruction) and function (metatranscriptomics, metaproteomics |
| HFA401 Biometrical Methods in Animal Breeding | PhD | NMBU | NA | NA |
For topics we will follow the textbook RA Mrode: Linear Models for the Prediction of Animal Breeding Values, CAB Int. Extra emphasis will be on Multitrait Mixed Models, and Random Regression etc. Relations to machine learning will be sketched. |
| PENG9560 Topics in Artificial Intelligence and Machine Learning | PhD | OsloMet | NA | NA |
This course covers advanced topics in artificial intelligence and machine learning, both theory and practice, recent scientific papers and state-of-the-art techniques.The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year. |
| PENG9590 Advanced Topics in Robotics and Control | PhD | OsloMet | NA | NA |
The course covers topics selected for their particular relevance to the students' intended doctoral thesis. The material for the course is composed in collaboration with the thesis supervisor, and the course proceeds as a self-study under expert supervision. The course is completed by student giving a seminar on a particular topic within the scope of the course material. |
| DT8122 - Probabilistic Artificial Intelligence | PhD | NTNU | 7.5 | Herindrasana Ramampiaro |
The main outcome of the course is to learn the principles of probabilistic models and deep generative models in Machine Learning and Artificial Intelligence, and acquiring skills for using existing tools that implement those principles (probabilistic programming languages). |
| MR8500 - PhD topics in marine control and hybrid power systems | PhD | NTNU | 7.5 | Roger Skjetne |
The course is organized for PhD students to target advanced topics within the themes of marine control engineering and/or marine hybrid power systems. |
| MM8407 - Simulation Methods in Many-particle Systems | PhD | NTNU | 7.5 | Astrid Silvia de Wijn |
The course covers simulation methods in many-particle systems applied to, amongst others classical fluids and spin systems. The course provides a theoretical basis as well as practical experience with application of molecular-dynamics and Monte-Carlo simulations. |
| IØ8812 - Introduction to machine learning and AI methods with economic applications | PhD | NTNU | 2.5 | Rita Duarte Pimentel |
This course will give an overview of machine learning methods within the AI framework. Economic applications for the learned methods will be presented and explored. The main goal is that students without previous knowledge in the area of machine learning and AI can understand and apply the models in their research topic. Examples of relevant applications are customer and marketing segmentation, credit risk assessment, forecasting and fraud detection. |
| IØ8813 - Advanced course in economic applications of machine learning and AI | PhD | NTNU | 2.5 | Rita Duarte Pimentel |
This course will extend the knowledge in machine learning methods applied to economics, going beyond the traditional unsupervised and supervised methods. The main goal is that the students can understand and apply sophisticated models in economic applications. Examples of relevant applications are algorithm trading, portfolio optimization and dynamic pricing. |
| KJ8107 - Advanced Organic Optoelectronic Materials | PhD | NTNU | 7.5 | Solon Oikonomopoulos |
Advanced organic optoelectronic materials focuses on the structure-property relationships of some of the most identifiable different classes of materials that give them these properties that make them ideal candidates for optoelectronic applications such as chemical sensors, molecular switches and machines, energy converting technologies or artificial intelligence. |
| IMT6171 - Real-time AI for robotics and simulated environments | PhD | NTNU | 5 | Sule Yildirim-Yayilgan |
Content: Robotic control mechanisms, Simulation environments, Real time knowledge representation, Real time decision making and search, Real time scheduling and allocation, Decision making under uncertainty, Autonomous aerial, ground and underwater robots |
| PK8100 - Advanced Robotics | PhD | NTNU | 7.5 | Olav Egeland |
Knowledge on selected research topics in robotics. Skills in analysis on selected research topics in robotics. General competence in robotics research. |