This course is intended for language technology students who do not have linguistic background. The purpose of the course is to give an overview of the structure of Icelandic, with a special consideration to features which can be problematic for natural language processing. The main topics that will be covered are the sound system of Icelandic and phonetic transcription (IPA and SAMPA); the inflectional and derivational morphology of Icelandic with a special consideration to Part-of-Speech tagging and tagsets; and the syntactic structure of Icelandic with emphasis on both phrase structure and dependency parsing.

The course is first and foremost organized for students in language technology that have a background in linguistics (or humanities) but are not experienced in computer science. This course is most often taken in the same semester as the course “Computer Science 1a”. If someone with a different background is interested in the course, please contact the teacher for further information.  The course is taught alongside ÍSL333G Programming for the humanities at the BA-level and all students attend the same lectures but MA students get longer assignments than BA students.

The main goal of this course is to support students in taking their first step toward learning programming, help them to knack the basis and train them in solving simple but diverse assignments in language technology using Python. Besides, students will be introduced to a few text processing tools that can be used for natural language processing.

Programming in Python (for computations in engineering and science): Main commands and statements (computations, control statements, in- and output), definition and execution of functions, datatypes (numbers, matrices, strings, logical values, records), operations and built-in functions, array and matrix computation, file processing, statistics, graphics. Object-oriented programming: classes, objects, constructors and methods. Concepts associated with design and construction of program systems: Programming environment and practices, design and documentation of function and subroutine libraries, debugging and testing of programmes.

Directed Study

Directed Study

Training in various aspects of the writing and editing of scientific texts. Various kinds of texts (non-fiction) examined and evaluated. Training in reviewing and commenting on scientific texts and in other aspects of editorial work. The main emphasis will be on the writing of articles, but other kinds of texts will also be considered, both shorter (conference abstracts, reviews) and longer (theses, books), as well as research proposals. Discussion of guidelines for the preparation of manuscripts. Types of plagiarism and how to avoid them and find them. Texts on different subjects will be used as examples, especially writings in linguistics, literature and history. The book Skrifaðu bæði skýrt og rétt will be used as a textbook (Höskuldur Þráinsson 2015).

This course is open to students of many MA programmes in the School of Humanities, cf. the regulations of the individual subjects. Students in the MA programmes in Icelandic literature, Icelandic linguistics, Icelandic studies and Icelandic teaching can take the course as part of the MA course requirements in Icelandic literature or Icelandic linguistics. Students in the MA programme in Icelandic teaching can, however, not have this course as the only linguistics or literature course in their MA.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbours, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course, the students work on a practical machine learning project.

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

This course will be dedicated to the Computer Assisted Translation-technology available to translators. Students get an insight into the importance of translation memories, how humans and machines use these memories, and learn how to align text corpora to create language data and dictionaries. How to use online dictionaries, data bases and other online means. We will consider language policy, technical terms and neologisms. The translators working environment will be considered as well as skills that help freelancers get by in the gig-economy. It is hoped that experienced translators will contribute to the seminar. Students work on projects during class to prepare them for the home assignments.

When we call someone we know well on the phone, it only  takes a few seconds to get a sense of how they are, for example whether they’ve just woken up, are upset, or have a cold. We might even hear that they are smiling, all based on cues in their voice, speech and language use. What if we could harness and measure this information? What other cues are present when we speak? Are there signs of undiagnosed diseases or developmental disorders that are otherwise difficult to evaluate? Recent research suggests this is the case.  Language samples, recordings of people talking, can contain indicators of whether someone is in the early stages of a neurodegenerative disease like Alzheimer’s, Parkinson’s or ALS, even before the individual notices symptoms.

Furthermore, speech samples are one of the most precise tools in a speech-language pathologist’s toolkit, allowing the detection of various nuances in language use that other tools, such as standardized tests, may miss. This is particularly important when diagnosing developmental language disorders in multilingual children, as developing reliable measurements for them has proven difficult. Rapid advances in language technology over the past decade have revolutionized the field of clinical linguistics, leading to various health-tech solutions that automatically analyze speech and language. 

But do these technological solutions work across different languages? Are the symptoms of developmental language disorders and neurodegenerative diseases the same in Icelandic as in other languages? What can the answers to these questions tell us about human language and cognition? These questions will form the core of this workshop, where students will have the opportunity to participate in ongoing research in the fields of clinical linguistics and health technology under the guidance of the instructors.

More information on workshops can be found here.

The course is designed for MA students in general and Icelandic linguistics and is also useful for other MA students that plan to conduct linguistics research. The course will cover the main research methods in linguistics, both in regards to experimental and natural data. We will discuss the fundamentals of the design of judgment tasks, fill-ins, elicitation tasks, behvioural and neuroimaging experiments and search in corpora such as the Icelandic Gigaword Corpus and IcePaHC. Research methods in diverse domains will be introduced, including syntax, phonology, sociolinguistics, historical linguistics, psycholinguistics, interactional linguistics and more. Finally we will discuss data analysis and interpretation of results, the pros and cons of differerent research methods and ethical considerations in linguistics.

Directed Study

Directed Study

In this course we concern ourselves with how language and society interact by examining sociolinguistic methods and concepts with regard to international and domestic research in the field.

Among the topics discussed are language attitudes, language contact, dialects, language style and language management. We take a look at different manifestations of language use and language variation as well as contemplating on how factors such as environment, context and background of a language user potentially influence language use and choice of style.

We provide an overview of principal research methods, both quantitative and qualitative, discuss recent trends in sociolinguistics and evaluate methods and methodologies with respect to the particular research topic.

Besides presenting research on attitudes towards language and language use, considering both attitudes towards one’s own language and that of others, we consider possible outcomes of unconventional language use. Special emphasis will be put on considering the language use of those who speak Icelandic as a foreign language as well as the status of immigrants in Iceland.

Additionally, we address the current status of the Icelandic language, particularly in relation to English and other languages. Principles of language management are discussed along with people’s ideas and believes about language through time. In that respect, we have a look at Icelandic language policy, language management, language standardization and linguistic purism from different perspectives, e.g. a synchronic and diachronic angle as well as with regard to other speech communities. 

We will discuss language use of particular social groups (e.g. teenagers) in terms of its social meaning for the group on the one hand and for the speech community as a whole on the other hand.

Students are expected to complete group or individual tasks on questions and problems originating from topics and discussions in the class room.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

The goal of this course is to strengthen the studentsʼ understanding of syntax by comparing selected phenomena in the syntactic structure of Icelandic to corrsesponding phenomena in other languages, both related and unrelated. It is assumed that all students have some knowledge of syntax, but a special attempt will be made to accommodate students with different background and expectations, even by splitting the group up into sections according to their previous knowledge of syntax and interest. Thus the course is meant to be suitable to graduate students of Icelandic and general linguistics, who mainly have theoretical interest in syntax, as well as to students of other languages, students in the School of Education and studdents of translation theory, provided that they have some basic knowledge of syntax.

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Directed Study

Directed Study

Training in various aspects of the writing and editing of scientific texts. Various kinds of texts (non-fiction) examined and evaluated. Training in reviewing and commenting on scientific texts and in other aspects of editorial work. The main emphasis will be on the writing of articles, but other kinds of texts will also be considered, both shorter (conference abstracts, reviews) and longer (theses, books), as well as research proposals. Discussion of guidelines for the preparation of manuscripts. Types of plagiarism and how to avoid them and find them. Texts on different subjects will be used as examples, especially writings in linguistics, literature and history. The book Skrifaðu bæði skýrt og rétt will be used as a textbook (Höskuldur Þráinsson 2015).

This course is open to students of many MA programmes in the School of Humanities, cf. the regulations of the individual subjects. Students in the MA programmes in Icelandic literature, Icelandic linguistics, Icelandic studies and Icelandic teaching can take the course as part of the MA course requirements in Icelandic literature or Icelandic linguistics. Students in the MA programme in Icelandic teaching can, however, not have this course as the only linguistics or literature course in their MA.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbours, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course, the students work on a practical machine learning project.

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

This course will be dedicated to the Computer Assisted Translation-technology available to translators. Students get an insight into the importance of translation memories, how humans and machines use these memories, and learn how to align text corpora to create language data and dictionaries. How to use online dictionaries, data bases and other online means. We will consider language policy, technical terms and neologisms. The translators working environment will be considered as well as skills that help freelancers get by in the gig-economy. It is hoped that experienced translators will contribute to the seminar. Students work on projects during class to prepare them for the home assignments.

When we call someone we know well on the phone, it only  takes a few seconds to get a sense of how they are, for example whether they’ve just woken up, are upset, or have a cold. We might even hear that they are smiling, all based on cues in their voice, speech and language use. What if we could harness and measure this information? What other cues are present when we speak? Are there signs of undiagnosed diseases or developmental disorders that are otherwise difficult to evaluate? Recent research suggests this is the case.  Language samples, recordings of people talking, can contain indicators of whether someone is in the early stages of a neurodegenerative disease like Alzheimer’s, Parkinson’s or ALS, even before the individual notices symptoms.

Furthermore, speech samples are one of the most precise tools in a speech-language pathologist’s toolkit, allowing the detection of various nuances in language use that other tools, such as standardized tests, may miss. This is particularly important when diagnosing developmental language disorders in multilingual children, as developing reliable measurements for them has proven difficult. Rapid advances in language technology over the past decade have revolutionized the field of clinical linguistics, leading to various health-tech solutions that automatically analyze speech and language. 

But do these technological solutions work across different languages? Are the symptoms of developmental language disorders and neurodegenerative diseases the same in Icelandic as in other languages? What can the answers to these questions tell us about human language and cognition? These questions will form the core of this workshop, where students will have the opportunity to participate in ongoing research in the fields of clinical linguistics and health technology under the guidance of the instructors.

More information on workshops can be found here.

The course is designed for MA students in general and Icelandic linguistics and is also useful for other MA students that plan to conduct linguistics research. The course will cover the main research methods in linguistics, both in regards to experimental and natural data. We will discuss the fundamentals of the design of judgment tasks, fill-ins, elicitation tasks, behvioural and neuroimaging experiments and search in corpora such as the Icelandic Gigaword Corpus and IcePaHC. Research methods in diverse domains will be introduced, including syntax, phonology, sociolinguistics, historical linguistics, psycholinguistics, interactional linguistics and more. Finally we will discuss data analysis and interpretation of results, the pros and cons of differerent research methods and ethical considerations in linguistics.

Final project

Directed Study

Directed Study

In this course we concern ourselves with how language and society interact by examining sociolinguistic methods and concepts with regard to international and domestic research in the field.

Among the topics discussed are language attitudes, language contact, dialects, language style and language management. We take a look at different manifestations of language use and language variation as well as contemplating on how factors such as environment, context and background of a language user potentially influence language use and choice of style.

We provide an overview of principal research methods, both quantitative and qualitative, discuss recent trends in sociolinguistics and evaluate methods and methodologies with respect to the particular research topic.

Besides presenting research on attitudes towards language and language use, considering both attitudes towards one’s own language and that of others, we consider possible outcomes of unconventional language use. Special emphasis will be put on considering the language use of those who speak Icelandic as a foreign language as well as the status of immigrants in Iceland.

Additionally, we address the current status of the Icelandic language, particularly in relation to English and other languages. Principles of language management are discussed along with people’s ideas and believes about language through time. In that respect, we have a look at Icelandic language policy, language management, language standardization and linguistic purism from different perspectives, e.g. a synchronic and diachronic angle as well as with regard to other speech communities. 

We will discuss language use of particular social groups (e.g. teenagers) in terms of its social meaning for the group on the one hand and for the speech community as a whole on the other hand.

Students are expected to complete group or individual tasks on questions and problems originating from topics and discussions in the class room.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

The goal of this course is to strengthen the studentsʼ understanding of syntax by comparing selected phenomena in the syntactic structure of Icelandic to corrsesponding phenomena in other languages, both related and unrelated. It is assumed that all students have some knowledge of syntax, but a special attempt will be made to accommodate students with different background and expectations, even by splitting the group up into sections according to their previous knowledge of syntax and interest. Thus the course is meant to be suitable to graduate students of Icelandic and general linguistics, who mainly have theoretical interest in syntax, as well as to students of other languages, students in the School of Education and studdents of translation theory, provided that they have some basic knowledge of syntax.

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Final project

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course provides an introduction to the methods at the heart of data science and introduces widely used software tools such as numpy, pandas, matplotlib and scikit-learn.

The course consists of 6 modules:

1. Introduction to the Python programming language.
2. Data wrangling and data preprocessing.
3. Exploratory data analysis and visualization.
4. Optimization.
5. Clustering and dimensionality reduction.
6. Regression and classification.

Each module concludes with a student project.

Note that there is an academic overlap with REI201G Mathematics and Scientific Computing and both courses cannot be valid for the same degree.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

This course is intended for language technology students who do not have linguistic background. The purpose of the course is to give an overview of the structure of Icelandic, with a special consideration to features which can be problematic for natural language processing. The main topics that will be covered are the sound system of Icelandic and phonetic transcription (IPA and SAMPA); the inflectional and derivational morphology of Icelandic with a special consideration to Part-of-Speech tagging and tagsets; and the syntactic structure of Icelandic with emphasis on both phrase structure and dependency parsing.

The course is first and foremost organized for students in language technology that have a background in linguistics (or humanities) but are not experienced in computer science. This course is most often taken in the same semester as the course “Computer Science 1a”. If someone with a different background is interested in the course, please contact the teacher for further information.  The course is taught alongside ÍSL333G Programming for the humanities at the BA-level and all students attend the same lectures but MA students get longer assignments than BA students.

The main goal of this course is to support students in taking their first step toward learning programming, help them to knack the basis and train them in solving simple but diverse assignments in language technology using Python. Besides, students will be introduced to a few text processing tools that can be used for natural language processing.

Programming in Python (for computations in engineering and science): Main commands and statements (computations, control statements, in- and output), definition and execution of functions, datatypes (numbers, matrices, strings, logical values, records), operations and built-in functions, array and matrix computation, file processing, statistics, graphics. Object-oriented programming: classes, objects, constructors and methods. Concepts associated with design and construction of program systems: Programming environment and practices, design and documentation of function and subroutine libraries, debugging and testing of programmes.

Directed Study

Directed Study

Training in various aspects of the writing and editing of scientific texts. Various kinds of texts (non-fiction) examined and evaluated. Training in reviewing and commenting on scientific texts and in other aspects of editorial work. The main emphasis will be on the writing of articles, but other kinds of texts will also be considered, both shorter (conference abstracts, reviews) and longer (theses, books), as well as research proposals. Discussion of guidelines for the preparation of manuscripts. Types of plagiarism and how to avoid them and find them. Texts on different subjects will be used as examples, especially writings in linguistics, literature and history. The book Skrifaðu bæði skýrt og rétt will be used as a textbook (Höskuldur Þráinsson 2015).

This course is open to students of many MA programmes in the School of Humanities, cf. the regulations of the individual subjects. Students in the MA programmes in Icelandic literature, Icelandic linguistics, Icelandic studies and Icelandic teaching can take the course as part of the MA course requirements in Icelandic literature or Icelandic linguistics. Students in the MA programme in Icelandic teaching can, however, not have this course as the only linguistics or literature course in their MA.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbours, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course, the students work on a practical machine learning project.

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

This course will be dedicated to the Computer Assisted Translation-technology available to translators. Students get an insight into the importance of translation memories, how humans and machines use these memories, and learn how to align text corpora to create language data and dictionaries. How to use online dictionaries, data bases and other online means. We will consider language policy, technical terms and neologisms. The translators working environment will be considered as well as skills that help freelancers get by in the gig-economy. It is hoped that experienced translators will contribute to the seminar. Students work on projects during class to prepare them for the home assignments.

When we call someone we know well on the phone, it only  takes a few seconds to get a sense of how they are, for example whether they’ve just woken up, are upset, or have a cold. We might even hear that they are smiling, all based on cues in their voice, speech and language use. What if we could harness and measure this information? What other cues are present when we speak? Are there signs of undiagnosed diseases or developmental disorders that are otherwise difficult to evaluate? Recent research suggests this is the case.  Language samples, recordings of people talking, can contain indicators of whether someone is in the early stages of a neurodegenerative disease like Alzheimer’s, Parkinson’s or ALS, even before the individual notices symptoms.

Furthermore, speech samples are one of the most precise tools in a speech-language pathologist’s toolkit, allowing the detection of various nuances in language use that other tools, such as standardized tests, may miss. This is particularly important when diagnosing developmental language disorders in multilingual children, as developing reliable measurements for them has proven difficult. Rapid advances in language technology over the past decade have revolutionized the field of clinical linguistics, leading to various health-tech solutions that automatically analyze speech and language. 

But do these technological solutions work across different languages? Are the symptoms of developmental language disorders and neurodegenerative diseases the same in Icelandic as in other languages? What can the answers to these questions tell us about human language and cognition? These questions will form the core of this workshop, where students will have the opportunity to participate in ongoing research in the fields of clinical linguistics and health technology under the guidance of the instructors.

More information on workshops can be found here.

The course is designed for MA students in general and Icelandic linguistics and is also useful for other MA students that plan to conduct linguistics research. The course will cover the main research methods in linguistics, both in regards to experimental and natural data. We will discuss the fundamentals of the design of judgment tasks, fill-ins, elicitation tasks, behvioural and neuroimaging experiments and search in corpora such as the Icelandic Gigaword Corpus and IcePaHC. Research methods in diverse domains will be introduced, including syntax, phonology, sociolinguistics, historical linguistics, psycholinguistics, interactional linguistics and more. Finally we will discuss data analysis and interpretation of results, the pros and cons of differerent research methods and ethical considerations in linguistics.

Directed Study

Directed Study

In this course we concern ourselves with how language and society interact by examining sociolinguistic methods and concepts with regard to international and domestic research in the field.

Among the topics discussed are language attitudes, language contact, dialects, language style and language management. We take a look at different manifestations of language use and language variation as well as contemplating on how factors such as environment, context and background of a language user potentially influence language use and choice of style.

We provide an overview of principal research methods, both quantitative and qualitative, discuss recent trends in sociolinguistics and evaluate methods and methodologies with respect to the particular research topic.

Besides presenting research on attitudes towards language and language use, considering both attitudes towards one’s own language and that of others, we consider possible outcomes of unconventional language use. Special emphasis will be put on considering the language use of those who speak Icelandic as a foreign language as well as the status of immigrants in Iceland.

Additionally, we address the current status of the Icelandic language, particularly in relation to English and other languages. Principles of language management are discussed along with people’s ideas and believes about language through time. In that respect, we have a look at Icelandic language policy, language management, language standardization and linguistic purism from different perspectives, e.g. a synchronic and diachronic angle as well as with regard to other speech communities. 

We will discuss language use of particular social groups (e.g. teenagers) in terms of its social meaning for the group on the one hand and for the speech community as a whole on the other hand.

Students are expected to complete group or individual tasks on questions and problems originating from topics and discussions in the class room.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

The goal of this course is to strengthen the studentsʼ understanding of syntax by comparing selected phenomena in the syntactic structure of Icelandic to corrsesponding phenomena in other languages, both related and unrelated. It is assumed that all students have some knowledge of syntax, but a special attempt will be made to accommodate students with different background and expectations, even by splitting the group up into sections according to their previous knowledge of syntax and interest. Thus the course is meant to be suitable to graduate students of Icelandic and general linguistics, who mainly have theoretical interest in syntax, as well as to students of other languages, students in the School of Education and studdents of translation theory, provided that they have some basic knowledge of syntax.

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Directed Study

Directed Study

Training in various aspects of the writing and editing of scientific texts. Various kinds of texts (non-fiction) examined and evaluated. Training in reviewing and commenting on scientific texts and in other aspects of editorial work. The main emphasis will be on the writing of articles, but other kinds of texts will also be considered, both shorter (conference abstracts, reviews) and longer (theses, books), as well as research proposals. Discussion of guidelines for the preparation of manuscripts. Types of plagiarism and how to avoid them and find them. Texts on different subjects will be used as examples, especially writings in linguistics, literature and history. The book Skrifaðu bæði skýrt og rétt will be used as a textbook (Höskuldur Þráinsson 2015).

This course is open to students of many MA programmes in the School of Humanities, cf. the regulations of the individual subjects. Students in the MA programmes in Icelandic literature, Icelandic linguistics, Icelandic studies and Icelandic teaching can take the course as part of the MA course requirements in Icelandic literature or Icelandic linguistics. Students in the MA programme in Icelandic teaching can, however, not have this course as the only linguistics or literature course in their MA.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbours, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course, the students work on a practical machine learning project.

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

This course will be dedicated to the Computer Assisted Translation-technology available to translators. Students get an insight into the importance of translation memories, how humans and machines use these memories, and learn how to align text corpora to create language data and dictionaries. How to use online dictionaries, data bases and other online means. We will consider language policy, technical terms and neologisms. The translators working environment will be considered as well as skills that help freelancers get by in the gig-economy. It is hoped that experienced translators will contribute to the seminar. Students work on projects during class to prepare them for the home assignments.

When we call someone we know well on the phone, it only  takes a few seconds to get a sense of how they are, for example whether they’ve just woken up, are upset, or have a cold. We might even hear that they are smiling, all based on cues in their voice, speech and language use. What if we could harness and measure this information? What other cues are present when we speak? Are there signs of undiagnosed diseases or developmental disorders that are otherwise difficult to evaluate? Recent research suggests this is the case.  Language samples, recordings of people talking, can contain indicators of whether someone is in the early stages of a neurodegenerative disease like Alzheimer’s, Parkinson’s or ALS, even before the individual notices symptoms.

Furthermore, speech samples are one of the most precise tools in a speech-language pathologist’s toolkit, allowing the detection of various nuances in language use that other tools, such as standardized tests, may miss. This is particularly important when diagnosing developmental language disorders in multilingual children, as developing reliable measurements for them has proven difficult. Rapid advances in language technology over the past decade have revolutionized the field of clinical linguistics, leading to various health-tech solutions that automatically analyze speech and language. 

But do these technological solutions work across different languages? Are the symptoms of developmental language disorders and neurodegenerative diseases the same in Icelandic as in other languages? What can the answers to these questions tell us about human language and cognition? These questions will form the core of this workshop, where students will have the opportunity to participate in ongoing research in the fields of clinical linguistics and health technology under the guidance of the instructors.

More information on workshops can be found here.

The course is designed for MA students in general and Icelandic linguistics and is also useful for other MA students that plan to conduct linguistics research. The course will cover the main research methods in linguistics, both in regards to experimental and natural data. We will discuss the fundamentals of the design of judgment tasks, fill-ins, elicitation tasks, behvioural and neuroimaging experiments and search in corpora such as the Icelandic Gigaword Corpus and IcePaHC. Research methods in diverse domains will be introduced, including syntax, phonology, sociolinguistics, historical linguistics, psycholinguistics, interactional linguistics and more. Finally we will discuss data analysis and interpretation of results, the pros and cons of differerent research methods and ethical considerations in linguistics.

Final project

Directed Study

Directed Study

In this course we concern ourselves with how language and society interact by examining sociolinguistic methods and concepts with regard to international and domestic research in the field.

Among the topics discussed are language attitudes, language contact, dialects, language style and language management. We take a look at different manifestations of language use and language variation as well as contemplating on how factors such as environment, context and background of a language user potentially influence language use and choice of style.

We provide an overview of principal research methods, both quantitative and qualitative, discuss recent trends in sociolinguistics and evaluate methods and methodologies with respect to the particular research topic.

Besides presenting research on attitudes towards language and language use, considering both attitudes towards one’s own language and that of others, we consider possible outcomes of unconventional language use. Special emphasis will be put on considering the language use of those who speak Icelandic as a foreign language as well as the status of immigrants in Iceland.

Additionally, we address the current status of the Icelandic language, particularly in relation to English and other languages. Principles of language management are discussed along with people’s ideas and believes about language through time. In that respect, we have a look at Icelandic language policy, language management, language standardization and linguistic purism from different perspectives, e.g. a synchronic and diachronic angle as well as with regard to other speech communities. 

We will discuss language use of particular social groups (e.g. teenagers) in terms of its social meaning for the group on the one hand and for the speech community as a whole on the other hand.

Students are expected to complete group or individual tasks on questions and problems originating from topics and discussions in the class room.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

The goal of this course is to strengthen the studentsʼ understanding of syntax by comparing selected phenomena in the syntactic structure of Icelandic to corrsesponding phenomena in other languages, both related and unrelated. It is assumed that all students have some knowledge of syntax, but a special attempt will be made to accommodate students with different background and expectations, even by splitting the group up into sections according to their previous knowledge of syntax and interest. Thus the course is meant to be suitable to graduate students of Icelandic and general linguistics, who mainly have theoretical interest in syntax, as well as to students of other languages, students in the School of Education and studdents of translation theory, provided that they have some basic knowledge of syntax.

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Final project

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course provides an introduction to the methods at the heart of data science and introduces widely used software tools such as numpy, pandas, matplotlib and scikit-learn.

The course consists of 6 modules:

1. Introduction to the Python programming language.
2. Data wrangling and data preprocessing.
3. Exploratory data analysis and visualization.
4. Optimization.
5. Clustering and dimensionality reduction.
6. Regression and classification.

Each module concludes with a student project.

Note that there is an academic overlap with REI201G Mathematics and Scientific Computing and both courses cannot be valid for the same degree.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

This course is intended for language technology students who do not have linguistic background. The purpose of the course is to give an overview of the structure of Icelandic, with a special consideration to features which can be problematic for natural language processing. The main topics that will be covered are the sound system of Icelandic and phonetic transcription (IPA and SAMPA); the inflectional and derivational morphology of Icelandic with a special consideration to Part-of-Speech tagging and tagsets; and the syntactic structure of Icelandic with emphasis on both phrase structure and dependency parsing.

The course is first and foremost organized for students in language technology that have a background in linguistics (or humanities) but are not experienced in computer science. This course is most often taken in the same semester as the course “Computer Science 1a”. If someone with a different background is interested in the course, please contact the teacher for further information.  The course is taught alongside ÍSL333G Programming for the humanities at the BA-level and all students attend the same lectures but MA students get longer assignments than BA students.

The main goal of this course is to support students in taking their first step toward learning programming, help them to knack the basis and train them in solving simple but diverse assignments in language technology using Python. Besides, students will be introduced to a few text processing tools that can be used for natural language processing.

Programming in Python (for computations in engineering and science): Main commands and statements (computations, control statements, in- and output), definition and execution of functions, datatypes (numbers, matrices, strings, logical values, records), operations and built-in functions, array and matrix computation, file processing, statistics, graphics. Object-oriented programming: classes, objects, constructors and methods. Concepts associated with design and construction of program systems: Programming environment and practices, design and documentation of function and subroutine libraries, debugging and testing of programmes.

Directed Study

Directed Study

Training in various aspects of the writing and editing of scientific texts. Various kinds of texts (non-fiction) examined and evaluated. Training in reviewing and commenting on scientific texts and in other aspects of editorial work. The main emphasis will be on the writing of articles, but other kinds of texts will also be considered, both shorter (conference abstracts, reviews) and longer (theses, books), as well as research proposals. Discussion of guidelines for the preparation of manuscripts. Types of plagiarism and how to avoid them and find them. Texts on different subjects will be used as examples, especially writings in linguistics, literature and history. The book Skrifaðu bæði skýrt og rétt will be used as a textbook (Höskuldur Þráinsson 2015).

This course is open to students of many MA programmes in the School of Humanities, cf. the regulations of the individual subjects. Students in the MA programmes in Icelandic literature, Icelandic linguistics, Icelandic studies and Icelandic teaching can take the course as part of the MA course requirements in Icelandic literature or Icelandic linguistics. Students in the MA programme in Icelandic teaching can, however, not have this course as the only linguistics or literature course in their MA.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbours, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course, the students work on a practical machine learning project.

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

This course will be dedicated to the Computer Assisted Translation-technology available to translators. Students get an insight into the importance of translation memories, how humans and machines use these memories, and learn how to align text corpora to create language data and dictionaries. How to use online dictionaries, data bases and other online means. We will consider language policy, technical terms and neologisms. The translators working environment will be considered as well as skills that help freelancers get by in the gig-economy. It is hoped that experienced translators will contribute to the seminar. Students work on projects during class to prepare them for the home assignments.

When we call someone we know well on the phone, it only  takes a few seconds to get a sense of how they are, for example whether they’ve just woken up, are upset, or have a cold. We might even hear that they are smiling, all based on cues in their voice, speech and language use. What if we could harness and measure this information? What other cues are present when we speak? Are there signs of undiagnosed diseases or developmental disorders that are otherwise difficult to evaluate? Recent research suggests this is the case.  Language samples, recordings of people talking, can contain indicators of whether someone is in the early stages of a neurodegenerative disease like Alzheimer’s, Parkinson’s or ALS, even before the individual notices symptoms.

Furthermore, speech samples are one of the most precise tools in a speech-language pathologist’s toolkit, allowing the detection of various nuances in language use that other tools, such as standardized tests, may miss. This is particularly important when diagnosing developmental language disorders in multilingual children, as developing reliable measurements for them has proven difficult. Rapid advances in language technology over the past decade have revolutionized the field of clinical linguistics, leading to various health-tech solutions that automatically analyze speech and language. 

But do these technological solutions work across different languages? Are the symptoms of developmental language disorders and neurodegenerative diseases the same in Icelandic as in other languages? What can the answers to these questions tell us about human language and cognition? These questions will form the core of this workshop, where students will have the opportunity to participate in ongoing research in the fields of clinical linguistics and health technology under the guidance of the instructors.

More information on workshops can be found here.

The course is designed for MA students in general and Icelandic linguistics and is also useful for other MA students that plan to conduct linguistics research. The course will cover the main research methods in linguistics, both in regards to experimental and natural data. We will discuss the fundamentals of the design of judgment tasks, fill-ins, elicitation tasks, behvioural and neuroimaging experiments and search in corpora such as the Icelandic Gigaword Corpus and IcePaHC. Research methods in diverse domains will be introduced, including syntax, phonology, sociolinguistics, historical linguistics, psycholinguistics, interactional linguistics and more. Finally we will discuss data analysis and interpretation of results, the pros and cons of differerent research methods and ethical considerations in linguistics.

Directed Study

Directed Study

In this course we concern ourselves with how language and society interact by examining sociolinguistic methods and concepts with regard to international and domestic research in the field.

Among the topics discussed are language attitudes, language contact, dialects, language style and language management. We take a look at different manifestations of language use and language variation as well as contemplating on how factors such as environment, context and background of a language user potentially influence language use and choice of style.

We provide an overview of principal research methods, both quantitative and qualitative, discuss recent trends in sociolinguistics and evaluate methods and methodologies with respect to the particular research topic.

Besides presenting research on attitudes towards language and language use, considering both attitudes towards one’s own language and that of others, we consider possible outcomes of unconventional language use. Special emphasis will be put on considering the language use of those who speak Icelandic as a foreign language as well as the status of immigrants in Iceland.

Additionally, we address the current status of the Icelandic language, particularly in relation to English and other languages. Principles of language management are discussed along with people’s ideas and believes about language through time. In that respect, we have a look at Icelandic language policy, language management, language standardization and linguistic purism from different perspectives, e.g. a synchronic and diachronic angle as well as with regard to other speech communities. 

We will discuss language use of particular social groups (e.g. teenagers) in terms of its social meaning for the group on the one hand and for the speech community as a whole on the other hand.

Students are expected to complete group or individual tasks on questions and problems originating from topics and discussions in the class room.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

The goal of this course is to strengthen the studentsʼ understanding of syntax by comparing selected phenomena in the syntactic structure of Icelandic to corrsesponding phenomena in other languages, both related and unrelated. It is assumed that all students have some knowledge of syntax, but a special attempt will be made to accommodate students with different background and expectations, even by splitting the group up into sections according to their previous knowledge of syntax and interest. Thus the course is meant to be suitable to graduate students of Icelandic and general linguistics, who mainly have theoretical interest in syntax, as well as to students of other languages, students in the School of Education and studdents of translation theory, provided that they have some basic knowledge of syntax.

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Directed Study

Directed Study

Training in various aspects of the writing and editing of scientific texts. Various kinds of texts (non-fiction) examined and evaluated. Training in reviewing and commenting on scientific texts and in other aspects of editorial work. The main emphasis will be on the writing of articles, but other kinds of texts will also be considered, both shorter (conference abstracts, reviews) and longer (theses, books), as well as research proposals. Discussion of guidelines for the preparation of manuscripts. Types of plagiarism and how to avoid them and find them. Texts on different subjects will be used as examples, especially writings in linguistics, literature and history. The book Skrifaðu bæði skýrt og rétt will be used as a textbook (Höskuldur Þráinsson 2015).

This course is open to students of many MA programmes in the School of Humanities, cf. the regulations of the individual subjects. Students in the MA programmes in Icelandic literature, Icelandic linguistics, Icelandic studies and Icelandic teaching can take the course as part of the MA course requirements in Icelandic literature or Icelandic linguistics. Students in the MA programme in Icelandic teaching can, however, not have this course as the only linguistics or literature course in their MA.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbours, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course, the students work on a practical machine learning project.

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

This course will be dedicated to the Computer Assisted Translation-technology available to translators. Students get an insight into the importance of translation memories, how humans and machines use these memories, and learn how to align text corpora to create language data and dictionaries. How to use online dictionaries, data bases and other online means. We will consider language policy, technical terms and neologisms. The translators working environment will be considered as well as skills that help freelancers get by in the gig-economy. It is hoped that experienced translators will contribute to the seminar. Students work on projects during class to prepare them for the home assignments.

When we call someone we know well on the phone, it only  takes a few seconds to get a sense of how they are, for example whether they’ve just woken up, are upset, or have a cold. We might even hear that they are smiling, all based on cues in their voice, speech and language use. What if we could harness and measure this information? What other cues are present when we speak? Are there signs of undiagnosed diseases or developmental disorders that are otherwise difficult to evaluate? Recent research suggests this is the case.  Language samples, recordings of people talking, can contain indicators of whether someone is in the early stages of a neurodegenerative disease like Alzheimer’s, Parkinson’s or ALS, even before the individual notices symptoms.

Furthermore, speech samples are one of the most precise tools in a speech-language pathologist’s toolkit, allowing the detection of various nuances in language use that other tools, such as standardized tests, may miss. This is particularly important when diagnosing developmental language disorders in multilingual children, as developing reliable measurements for them has proven difficult. Rapid advances in language technology over the past decade have revolutionized the field of clinical linguistics, leading to various health-tech solutions that automatically analyze speech and language. 

But do these technological solutions work across different languages? Are the symptoms of developmental language disorders and neurodegenerative diseases the same in Icelandic as in other languages? What can the answers to these questions tell us about human language and cognition? These questions will form the core of this workshop, where students will have the opportunity to participate in ongoing research in the fields of clinical linguistics and health technology under the guidance of the instructors.

More information on workshops can be found here.

The course is designed for MA students in general and Icelandic linguistics and is also useful for other MA students that plan to conduct linguistics research. The course will cover the main research methods in linguistics, both in regards to experimental and natural data. We will discuss the fundamentals of the design of judgment tasks, fill-ins, elicitation tasks, behvioural and neuroimaging experiments and search in corpora such as the Icelandic Gigaword Corpus and IcePaHC. Research methods in diverse domains will be introduced, including syntax, phonology, sociolinguistics, historical linguistics, psycholinguistics, interactional linguistics and more. Finally we will discuss data analysis and interpretation of results, the pros and cons of differerent research methods and ethical considerations in linguistics.

Final project

Directed Study

Directed Study

In this course we concern ourselves with how language and society interact by examining sociolinguistic methods and concepts with regard to international and domestic research in the field.

Among the topics discussed are language attitudes, language contact, dialects, language style and language management. We take a look at different manifestations of language use and language variation as well as contemplating on how factors such as environment, context and background of a language user potentially influence language use and choice of style.

We provide an overview of principal research methods, both quantitative and qualitative, discuss recent trends in sociolinguistics and evaluate methods and methodologies with respect to the particular research topic.

Besides presenting research on attitudes towards language and language use, considering both attitudes towards one’s own language and that of others, we consider possible outcomes of unconventional language use. Special emphasis will be put on considering the language use of those who speak Icelandic as a foreign language as well as the status of immigrants in Iceland.

Additionally, we address the current status of the Icelandic language, particularly in relation to English and other languages. Principles of language management are discussed along with people’s ideas and believes about language through time. In that respect, we have a look at Icelandic language policy, language management, language standardization and linguistic purism from different perspectives, e.g. a synchronic and diachronic angle as well as with regard to other speech communities. 

We will discuss language use of particular social groups (e.g. teenagers) in terms of its social meaning for the group on the one hand and for the speech community as a whole on the other hand.

Students are expected to complete group or individual tasks on questions and problems originating from topics and discussions in the class room.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.

The goal of this course is to strengthen the studentsʼ understanding of syntax by comparing selected phenomena in the syntactic structure of Icelandic to corrsesponding phenomena in other languages, both related and unrelated. It is assumed that all students have some knowledge of syntax, but a special attempt will be made to accommodate students with different background and expectations, even by splitting the group up into sections according to their previous knowledge of syntax and interest. Thus the course is meant to be suitable to graduate students of Icelandic and general linguistics, who mainly have theoretical interest in syntax, as well as to students of other languages, students in the School of Education and studdents of translation theory, provided that they have some basic knowledge of syntax.

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Final project

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course provides an introduction to the methods at the heart of data science and introduces widely used software tools such as numpy, pandas, matplotlib and scikit-learn.

The course consists of 6 modules:

1. Introduction to the Python programming language.
2. Data wrangling and data preprocessing.
3. Exploratory data analysis and visualization.
4. Optimization.
5. Clustering and dimensionality reduction.
6. Regression and classification.

Each module concludes with a student project.

Note that there is an academic overlap with REI201G Mathematics and Scientific Computing and both courses cannot be valid for the same degree.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

The purpose of this course is to introduce to students the role and utility of language resources (corpora), both for software development and for research on texts and speech. Available language corpora for Icelandic will be presented, and students will also gain insights into the composition of new corpora. The structure of these resources will be analyzed along with the opportunities and limitations associated with them. Students will work with the resources in an original manner and use them to develop new applications or new resources.