Applied Linear Statistical Models (STÆ312M)

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

R for beginners (MAS103M)

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and will learn how to apply statistical methods they know in R. Main topics are loading data, graphical representation, descriptive statistics and how to perform the most common hypothesis tests (t- test, chi-square test, etc.) in R. In addition, students will learn how to make reports using the knitr package.

The course is taught during a five week period. A teacher gives lectures and students work on a project in class.

Time Series Analysis (IÐN113F)

ARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.

Introduction to deep neural networks (TÖL506M)

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.

Bayesian Data Analysis (STÆ529M)

Goal: To train students in applying methods of Bayesian statistics for analysis of data. Topics: Theory of Bayesian inference, prior distributions, data distributions and posterior distributions. Bayesian inference  for parameters of univariate and multivariate distributions: binomial; normal; Poisson; exponential; multivariate normal; multinomial. Model checking and model comparison: Bayesian p-values; deviance information criterion (DIC). Bayesian computation: Markov chain Monte Carlo (MCMC) methods; the Gibbs sampler; the Metropolis-Hastings algorithm; convergence diagnostistics. Linear models: normal linear models; hierarchical linear models; generalized linear models. Emphasis on data analysis using software, e.g. Matlab and R.

R Programming (MAS102M)

Students will perform traditional statistical analysis on real data sets. Special focus will be on regression methods, including multiple regression analysis. Students will apply sophisticated methods of graphical representation and automatic reporting. Students will hand in a projects where they apply the above mentioned methods on real datasets in order to answer research questions

Machine Learning (REI505M)

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.

Mathematics for Finance I (HAG122F)

The course will cover the main points of statistics and pricing in finance. Emphasis is placed on introducing students to the use of statistical and mathematical methods to analyze, price and obtain information about financial instruments. Emphasis is placed on real examples where students are trained to solve tasks similar to those they may have to solve in their jobs in the financial market.

In the statistical part of the course, ideas about continuous and sparse probability distributions, expected values, variance and standard deviation, confidence intervals, null hypotheses and linear regression analyses, both simple and multivariate, will be reviewed. The basic ideas of the Capital Asset Pricing Model (CAPM) will also be reviewed.

The pricing part of the course will deal with the pricing of futures contracts on shares, bonds and currency and interest rate swaps, the construction of interest rate curves, as well as the types and characteristics of options.

Mathematics for Finance II (HAG122M)

The course will cover the main points of statistics and pricing in finance. Emphasis is placed on introducing students to the use of numerical and mathematical methods to analyze, price and obtain information about financial instruments. Emphasis is placed on real examples where students are trained to solve tasks similar to those they may have to solve in the workplace.

The statistical part of the course will cover time series analysis. There, models such as auto regressive models (e. Auto regressive model, AR model) and models with moving averages (e. Moving-average model, MA model) will be introduced to play. Also the combination of ARMA, ARIMA and SARIMA models. Finally, conditional variance models or ARCH and GARCH models will be reviewed.

In the pricing section, we will cover binomial trees, Wiener processes, Ito's auxiliary theorem, the Black-Scoles-Merton model and the pricing of stock and currency options.

Biostatistics II (Clinical Prediction Models ) (LÝÐ301F)

This course is a continuation of Biostatistics I and constitutes a practical guide to statistical analyses of student's own research projects. The course covers the following topics. Estimation of relative risk/odds ratios and adjusted estimation of relative risk/odds ratios, correlation and simple linear regression, multiple linear regression and logistic regression. This course is centered around two core principles that define its thematic focus: Prediction from Statistical Models
Students will learn to build models—such as logistic regression and Cox proportional hazards models—that estimate the probability of future medical events based on recorded data. Evaluation of Discrimination Capacity A key theme is assessing how well models distinguish between individuals with different outcomes, using metrics such as the Area Under the ROC Curve (AUC), concordance index, and related performance measures.

The course is based on lectures and practical sessions using R for statistical analyses.

Probability and Statistics (MAS201F)

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.

Applied data analysis (MAS202M)

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and the statistical software R. Students will learn to apply a broad range of statistical methods in R (such as classification methods, resampling methods, linear model selection and tree-based methods). The course on 12 weeks and will be on "flipped" form. This means that no lectures will be given but students will read some material and watch videos before attending classes. Students will then work on assignments during the classes.

Operations Research (IÐN401G)

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover linear programming and the simplex algorithm, as well as related analytical topics. It will also introduce special types of mathematical models, including transportation, assignment, network, and integer programming models. The student will become familiar with a modeling language for linear programming.

Survey research methods (FÉL089F)

The purpose of this course is to provide students with understanding on how to plan and conduct survey research. The course will address most common sampling design and different type of survey research (phone, face-to-face, internet, mail etc.). The basic measurement theories will be used to explore fundamental concepts of survey research, such as validity, reliability, question wording and contextual effect. The use of factor analysis and item analysis will be used to evaluate the quality of measurement instruments.  The course emphasizes students’ active learning by planning survey research and analyzing survey data.

Business Intelligence (IÐN610M)

Business intelligence are the strategies and technologies companies use to collect, interpret and utilize data for decision support. This course goes beyond reports and dashboards and demonstrates how artificial intelligence can help us gain insights and recommend action. The course is comprised of five learning modules: 1.) regression and classification where data is segmented accoring to predetermined labels. 2.)  semi- and un-labelled data where items are grouped based on similarity measures. 3.) Process mining. 4.) Natural language processing, and 5.) Data ethics. Within each learning module students prepare for class and work in teams on a business problem, followed by an individual assessment.

Econometrics III (HAG606G)

The aim of the first part of the course is to introduce students to various techniques used in time series analysis, such as models for financial data (ARCH/GARCH/SV models), models with time varying parameters, the Kalman filter, and Bayesian estimation. In the second half of the course the main techniques used in machine learning and data science are presented, such as factor models, ridge and LASSO regressions, classification methods, regression trees, clustering and natural language processing as time permits. The emphasis is on presenting the theoretical foundations of the course subjects and their practical implementation in data analysis. Recommended prerequisites are introductory courses on probability and statistics, econometrics, macroeconometrics and the ability to perform data analysis in R.

Biostatistics III (Survival analysis) (LÝÐ079F)

The course covers methods for analysis of cohort studies using methods for time to event or survival analysis. It is based on the course Biostat III – Survival analysis for epidemiologists in R at the Karolinska Institutet: See (https://biostat3.net/index.html): "Topics covered include methods for estimating patient survival (life table and Kaplan-Meier methods), comparing survival between patient subgroups (log-rank test), and modelling survival (primarily Poisson regression, Cox proportional hazards model and flexible parametric models). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification." 

Random Effects Models (STÆ004F)

The focus of this course is on Bayesian latent Gaussian models (BLGMs) which are a class of Bayesian hierarchical models and applications of these models. The main topics are three types of BLGMs: (i) Bayesian Gaussian—Gaussian models, (ii) BLGMs with a univariate link function, and (iii) BLGMs with a multivariate link function, as well as prior densities for BLGMs and posterior computation for BLGMs. In the first part of the course, the basics of these models is covered and homework assignments will be given on these topics. In the second part of the course, the focus is on a project, in which data are analyzed using BLGMs. Each student can contribute data that she or he wishes to analyze. The material in the course is based on a theoretical background. However, the focus on data analysis is strong, and computation and programming play a large role in the course. Thus, the course will be useful to students in their future projects involving data analysis.

Linear regression models, the multiple normal distribution, hierarchical models, fixed and random effect models, restricted maximum likelihood estimation, best linear unbiased estimators, Bayesian inference, statistical decision theory, Markov chains,  Monte Carlo integration, importance sampling, Markov chain Monte Carlo, Gibbs sampling, the Metropolis-Hastings algorithm.

The AI lifecycle (REI603M)

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.