Syllabi

Prerequisites (knowledge of topic)
Prior knowledge of quantitative research methods is required. Basic knowledge of SPSS, or willingness to learn SPSS individually.  


Hardware
Please bring your own laptop (mac or windows).

Software
Make sure to install SPSS version 27, Jamovi (free software) 2.3.28 and Process version 4.2 or newer on your computer before the start of the course.

Learning objectives

After the successful completion of this course, participants will:

• be well-equipped to design, conduct, and analyze experiments in a rigorous and informed manner
• understand how to update and improve their experiments and develop their skills in critically evaluating experimental research
• be able to run the appropriate analysis depending on the chosen experimental design
• understand the principles and the logic of the following statistical techniques: Regression, Analysis of Variance, Analysis of Covariance, Contrast Analysis, Mediation Analysis, Moderation Analysis, Logit
• understand issues related to statistical power and effect size and ethical considerations in experimental research.
• interpret the data of a wide range of experiments and report the results in a scientific paper. 

Course content
This dual-focused program is designed to equip participants from various disciplines (behavioral economics, management, medicine, health science, communication, marketing) with the necessary skills 1) to conduct rigorous experimental research and 2) to analyze data from various experimental designs. Against the backdrop of actual examples from practice and academia, each module of the course centers around a specific experimental design. Participants will learn how to improve these experimental designs and to conduct the appropriate data analysis techniques. The curriculum covers a diverse range of designs and corresponding analyses, including moderation, mediation, spotlight analyses, co-variate analyses, and logit. Participants will acquire a nuanced understanding of these techniques, their application in research, and learn how to report their findings in an academic article. A part of the course will be devoted to ethical considerations in experimental research, addressing contemporary concerns in research integrity. 

Structure

Day 1: 

• Improving experimental designs
• Experimental issues: Confounds, sampling, and demand effects
• Intuition behind ANOVA: Increasing systematic variance and reducing error variance
• Power and Effect size
• Research integrity issues: p-hacking, power-analyses, preregistration

Day 2:

• Experimental design with moderation
• ANOVA
• Simple effect analyses
• MANOVA

Day 3:

• Experimental design with repeated measures
• Repeated measures ANOVA
• Mixed design
• ANCOVA

Day 4:

• Experimental design with (moderated) mediation
• Simple and moderated mediation
• Process through moderation
• Causal chain model

Day 5:

• Experimental design with continuous moderator
• Regression
• Conditional process
• Spotlight and floodlight analyses
• Experimental design with binominal DV
• Logit regression

Literature

Mandatory

Book: 

• Field, Andy P. (2023). Discovering statistics using SPSS, Sixth Edition, London: Sage Publications: Chapters: 3, 11, 13, 14, 15, 16, 17, 20.

Articles: 

• Meyvis, T., & Van Osselaer, S. M. J. (2018), “Increasing the Power of Your Study by Increasing the Effect Size,”Journal of Consumer Research, 44 (5), 1157–1173.
• Pieters, R. (2017). Meaningful mediation analysis: Plausible causal inference and informative communication. Journal of Consumer Research, 44(3), 692-716.
• Simmons, Nelson, Simonsohn (2011) “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allow Presenting Anything as Significant”, Psychological Science, 22(11), 1359-1366.
• Spencer, S.J., Zanna, M.P., & Fong, G.T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89 (6), 845-851.
• Spiller, S. A., Fitzsimons, G. J., Lynch, J. G., Jr., & McClelland G. H. (2013), “Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression,” Journal of Marketing Research 50, 277-88.

Supplementary / voluntary

• Abbey, J. D., & Meloy, M. G. (2017). Attention by design: Using attention checks to detect inattentive respondents and improve data quality. Journal of Operations Management, 53, 63-70.Giner-Sorolla, R.,
• McShane, B. B., Bradlow, E. T., Lynch Jr, J. G., & Meyer, R. J. (2024). “Statistical Significance” and Statistical Reporting: Moving Beyond Binary. Journal of Marketing, 00222429231216910.
• Montoya, A. K., Reifman, A., Carpenter, T., Lewis Jr, N. A., Aberson, C. L., … & Soderberg, C. (2019). Power to detect what? Considerations for planning and evaluating sample size. Personality and Social Psychology Review, 10888683241228328.
• Vosgerau, J., Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2019). 99% impossible: A valid, or falsifiable, internal meta-analysis. Journal of Experimental Psychology: General, 148(9), 1628.

Examination part

• Homework delivered during week of the course (4 assignments, 40%)
• Homework delivered after the course (1 assignment, 60%)

Supplementary aids
Open book.

Examination content

Assignment 1 (Due Tuesday Morning):

• Improving an experimental design
• Detection of confounds

Assignment 2 (Due Wednesday Morning):

• ANOVA
• Simple effect tests
• Interpretation and report of results

Assignment 3 (Due Thursday Morning):

• Mixed design
• Assumptions
• Follow-up tests
• Interpretation and report of results

Assignment 4 (Due Friday Morning):

• Mediation
• Moderated mediaton
• Mediation with categorical IV
• Interpretation and report of results

Assignment 5 (Due within one month after end of course):

• Analyse data set using analyses covered during the course
• Report and interpret results as for an academic journal
• Critically reflect upon research design and propose improvements

Examination relevant literature
All mandatory literature indicated above. 

Prerequisites
The instructor will send students instructions on how to install qualitative software for in-class exercises. No previous knowledge of qualitative research is required. 

Hardware
NA

Software
We will spend one day learning a qualitative analysis software package:
GSERM St. Gallen: ATLAS.ti
GSERM Ljubljana: NVIVO
Virtual course: MAXQDA

The methods discussed in the course will be applicable to qualitative studies in a range of fields, including the behavioral sciences, social sciences, health sciences, communications, business, and marketing.

Learning objectives
The primary learning objectives are:

• Understand methodological options regarding research design, data collection, and analysis
• Understand approaches to developing an interview guide
• Gain skills in writing analytic memos, creating diagrams, and coding qualitative data
• Understand primary differences among research traditions: grounded theory, narrative analysis, and case study
• Gain skills in using qualitative analysis software

Course content
Qualitative Research Methods and Data Analysis presents strategies for analyzing and making sense of qualitative data. The course will discuss the qualitative inquiry continuum—from descriptive to interpretive—and present established qualitative research approaches such as grounded theory, narrative analysis, and case studies. The course will briefly cover research design, including discussion of design dimensions: time, comparison, and use of theory. We will briefly cover data collection strategies—primarily interviews and focus groups—but the course will largely focus on data analysis. In particular, we will consider how researchers develop codes and integrate memo writing and diagrams into a larger analytic process. Coding and memo writing are concurrent tasks that occur during an active review of interviews, documents, focus groups, and/or online data. 

Analytic memo writing is a strategy for capturing analytical thinking, inscribed meaning, and cumulative evidence for condensed meanings. Memos can also resemble early writing for reports, articles, chapters, and other forms of presentation. Researchers can also mine memos for codes and use memos to build evocative themes and theory. Coding provides an analytic focus and investigative point of view; the course will illustrate specific coding practices that generate particular types of topics and concepts, such as process codes and in vivo codes. We will discuss deductive and inductive coding and how a codebook evolves—how we discern emerging codes and assess conceptual shifts during analysis. Our discussion will move from managing codes to developing code hierarchies, identifying code clusters, and building multidimensional themes. We will discuss final research products—how results are framed to underscore cognitive empathy, precision, and emergent discovery. 

The course will also discuss using visual tools in analysis, such as diagramming key quotations from data to holistically present the participant’s key narratives. Visual tools can also assist in looking horizontally across many documents to identify and illustrative connective themes and link the parts (quotations or codes) to the whole (themes, documents, or participants).

The course will include daily in-class exercises—both individual and group—including exercises using software.  

Structure
Day 1
•    Core principles and practices in qualitative data inquiry
•    Qualitative research design
·    Overview of data types
·    Design dimensions: Comparison, time, theory
·    Sampling strategies
·    Triangulation
•    Data collection  
•    Interviews
•    Focus groups
•    Other types of data
•    Developing interview questions

Day 2
•    Analysis practices
o    Memo writing 
·    Document summary memos
·    Key-quote memos
·    Methods memos
o    Using visual tools
·    Data collection episode profiles
·    Making sense of data using diagrams
o    Coding qualitative data
·    Deductive vs. Inductive coding
·    Descriptive coding
·    Interpretive coding
·    Coding practices
·    Creating a codebook
 
Day 3
•    Introduction to qualitative software 
•    Writing comments and memos
•    Coding data
•    Developing a code system
•    Creating quotations diagrams
•    Analysis 
·    Exploring codes and memos 
·    Code co-occurrences
·    Codes and demographic variables
·    Matrices and diagrams
·    Blending quantitative and qualitative data

Day 4
•    Methodological traditions
•    Grounded theory
•    Narrative analysis
•    Case study
•    Generic qualitative analysis

Day 5
•    Comparison of methodological traditions
•    Qualitative research design: Revisiting strategies
•    In-class exercise: Study design
•    Evaluating qualitative articles
•    Class discussion

Literature

Suggested Reading (Articles)
Electronic version of these articles will be provided to registered participants:

Ahlsen, Birgitte, et al. 2013. “(Un)doing Gender in a Rehabilitation Context: A Narrative Analysis of Gender and Self in Stories or Chronic Muscle Pain.” Disability and Rehabilitation 1 8.

Charmaz, Kathy. 1999. “Stories of Suffering: Subjective Tales and Research Narratives.” Qualitative Health Research 9:362 82.

Sandelowski, Margarete. 2000. “Whatever Happened to Qualitative Description?” Research in Nursing and Health 23:334 40.

Rouch, Gareth, et al. 2010. “Public, Private and Personal: Qualitative Research on Policymakers’ Opinions on Smokefree Interventions to Protect Children in ‘Private’ Spaces.” BMC Public Health 10:797 807.

Suggested Reading (Books)
Charmaz, Kathy. 2006. Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.

Marshall, Catherine, and Gretchen B. Rossman. 2006. Designing Qualitative Research. 4th ed. Sage.

Yin, Robert. 2013. Case Study Research Design and Methods. Sage.

Examination part
Participants will be asked to read several interviews or journal entries and generate a preliminary analysis of the data using techniques discussed during the course. This examination will be due three weeks after the course ends.

Examination content
Students will have to demonstrate familiarity with the differences between grounded theory, narrative analysis, case study, and pragmatic analysis. The assignment will require them to choose one of these approaches to design a study and analyze several documents provided by the instructor. Their preliminary analysis will include memos, a codebook, diagrams, early findings, and reflection on next steps.

Prerequisites
Basic knowledge of statistics, data analysis, and programming.

Hardware
Participants need to bring laptops.

Software
Google Colaboratory environment (https://colab.research.google.com/). Participants will receive setup instructions before the course.

Learning objective
Large language models like ChatGPT or Llama-3 are incredibly useful tools for research in the social and behavioral sciences. In this course, you will (1) learn about the fundamental principles of large language models, (2) learn how to employ state-of-the-art open-access large language models using the Hugging Face ecosystem, (3) learn about the rich opportunities that large language models offer for behavioral and social science research, and (4) gain experience in applying large language models to answer personal research questions.

Course content
Open-access large language models (LLMs) like Llama-3 or Phi-3 are powerful alternatives to proprietary LLMs like ChatGPT that offer state-of-the-art performance and crucial advantages in terms of reproducibility and scale. This course introduces the use of open-access LLMs from the Hugging Face ecosystem for research in the behavioral and social sciences. In short lectures, participants will learn about key concepts (e.g., embeddings, causal attention, feature extraction, classification, prediction, fine-tuning, and token generation) and practical examples from social and behavioral science. In hands-on exercises, participants will apply language models to answer research questions from psychology, political science, decision-making research, and other fields. During and after the course, participants will engage in a personal research project applying large language models to a personal research question. Two lecturers will hold this course, implying a high level of support during the exercises and research project design. 

Structure
Day 1:
Morning: Welcome and intro to large language models

Afternoon: Applying the Hugging Face ecosystem for open-access large language models

Day 2: 
Morning: Intro to feature extraction and embedding models

Afternoon: Applying large language models to predict the relationships between survey items and questionnaires in personality psychology

Day 3: 
Morning: Intro to fine-tuning for classification and regression

Afternoon: Applying large language models to evaluate and classify texts in political science

Day 4: 
Morning: Intro to token and text generation

Afternoon: Using large language models to predict people’s responses in decision-making situations 

Day 5: 
Morning: Overview of additional applications of large language models for qualitative data analysis 

Afternoon: Project pitches

Literature

Mandatory:
Hussain, Z., Binz, M., Mata, R., & Wulff, D. U. (2023). A tutorial on open-source large language models for behavioral science. PsyArXiv (available in December)

Supplementary / voluntary:
Tunstall, L., von Werra, L., & Wolf T. (2022). Natural Language Processing with Transformers. John Wiley & Sons.

Examination
The course grade will be determined based on the quality of a project pitch at the end of the course and a two-page research paper submitted after the course. The paper communicates an analysis applying large language models to a personal research question, including all parts of a traditional research paper (introduction, method, results, and discussion). The research paper can be based on the examples during the course. 

Prerequisites (knowledge of topic)
Basic knowledge of Python programming and familiarity with Machine Learning

Hardware
A laptop

Software
A google account to access colab

Learning objectives
On successful completion of this course, students will be able to:

– Understand the fundamentals of Generative AI and Large Language Models.
– Understand how to Implement and fine-tune LLMs for various tasks.
– Evaluate the performance of LLMs using various metrics.
– Understand the ethical and societal implications of using LLMs.
– Develop their own projects utilizing Generative AI.

Structure

Day 1: Introduction to Generative AI

Generative vs. Discriminative Models

Overview of Generative AI

Applications of generative AI

Day 2: Basics of Language Models

Understanding Language Models

Unigram, Bigram, N-gram Models

Neural network based (seq2seq) Language Models

Day 3: Transformer Models and Attention Mechanism

Introduction to Transformer Models

Self-Attention and Multi-Head Attention

BERT

Day 4: Large Language Models (LLMs)

Introduction to LLMs

Architecture of GPT-4

Fine-tuning LLMs

Day 5: Evaluating and Improving LLMs

Evaluation metrics

Issues in LLMs

Prompt engineering

Examination
We will have a take-home exam.

Supplementary aids
Open Book

Examination Content
Students are expected to work on one individual project related to the application of LLMs on a specific domain (e.g., healthcare, finance, or social marketing).

Prerequisites (knowledge of topic)
Comfortable familiarity with univariate differential and integral calculus, basic probability theory, and linear algebra is required. Students should have completed Ph.D.-level courses in introductory statistics, and in linear and generalized linear regression models (including logistic regression, etc.), up to the level of Regression III. Familiarity with discrete and continuous univariate probability distributions will be helpful.

Hardware
Students will be required to provide their own laptop computers.

Software
All analyses will be conducted using the R statistical software. R is free, open-source, and runs on all contemporary operating systems. The instructor will also offer support for students wishing to use Stata.

Learning objectives
Students will learn how to visualize, analyze, and conduct diagnostics on models for observational data that has both cross-sectional and temporal variation.

Course content
Analysts increasingly find themselves presented with data that vary both over cross-sectional units and across time. Such panel data provides unique and valuable opportunities to address substantive questions in the economic, social, and behavioral sciences. This course will begin with a discussion of the relevant dimensions of variation in such data, and discuss some of the challenges and opportunities that such data provide. It will then progress to linear models for one-way unit effects (fixed, between, and random), models for complex panel error structures, dynamic panel models, nonlinear models for discrete dependent variables, and models that leverage panel data to make causal inferences in observational contexts. Students will learn the statistical theory behind the various models, details about estimation and inference, and techniques for the visualization and substantive interpretation of their statistical results. Students will also develop statistical software skills for fitting and interpreting the models in question, and will use the models in both simulated and real data applications. Students will leave the course with a thorough understanding of both the theoretical and practical aspects of conducting analyses of panel data.

Structure
Day One:
Morning:
•     (Very) Brief Review of Linear Regression
•     Overview of Panel Data: Visualization, Pooling, and Variation
•     Regression with Panel Data
Afternoon:
•     Unit Effects Models: Fixed-, Between-, and Random-Effects

Day Two:
Morning:
•     Dynamic Panel Data Models: The Instrumental Variables / Generalized Method of Moments Framework
Afternoon:
•     More Dynamic Models: Orthogonalization-Based Methods

Day Three:
Morning:
•     Unit-Effects and Dynamic Models for Discrete Dependent Variables
Afternoon:
•     GLMs for Panel Data: Generalized Estimating Equations (GEEs)

Day Four:
Morning:
•     Introduction to Causal Inference with Panel Data (Including Unit Effects)
Afternoon:
•     Models for Causal Inference: Differences-In-Differences, Synthetic Controls, and Other Methods

Day Five:
Morning:
•     Practical Issues: Model Selection, Specification, and Interpretation
Afternoon:
•     Course Examination

Literature

Mandatory
Hsiao, Cheng. 2014. Analysis of Panel Data, 3rd Ed. New York: Cambridge University Press.
OR
Croissant, Yves, and Giovanni Millo. 2018. Panel Data Econometrics with R. New York: Wiley.

Supplementary / voluntary
Abadie, Alberto. 2005. “Semiparametric Difference-in-Differences Estimators.” Review of Economic Studies 72:1-19.

Anderson, T. W., and C. Hsiao. 1981. “Estimation Of Dynamic Models With Error Components.” Journal of the American Statistical Association 76:598-606.

Antonakis, John, Samuel Bendahan, Philippe Jacquart, and Rafael Lalive. 2010. “On Making Causal Claims: A Review and Recommendations.” The Leadership Quarterly 21(6):1086-1120.

Arellano, M. and S. Bond. 1991. “Some Tests Of Specification For Panel Data: Monte Carlo Evidence And An Application To Employment Equations.” Review of Economic Studies 58:277-297.

Beck, Nathaniel, and Jonathan N. Katz. 1995. “What To Do (And Not To Do) With Time-Series Cross-Section Data.” American Political Science Review 89(September): 634-647.

Bliese, P. D., D. J. Schepker, S. M. Essman, and R. E. Ployhart. 2020. “Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data.” Journal of Management, 46(1):70-99.

Clark, Tom S. and Drew A. Linzer. 2015. “Should I Use Fixed Or Random Effects?” Political Science Research and Methods 3(2):399-408.

Doudchenko, Nikolay, and Guido Imbens. 2016. “Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis.” Working paper: Graduate School of Business, Stanford University.

Gaibulloev, K., Todd Sandler, and D. Sul. 2014. “Of Nickell Bias, Cross-Sectional Dependence, and Their Cures: Reply.” Political Analysis 22: 279-280.

Hill, T. D., A. P. Davis, J. M. Roos, and M. T. French. 2020. “Limitations of Fixed-Effects Models for Panel Data.” Sociological Perspectives 63:357-369.

Hu, F. B., J. Goldberg, D. Hedeker, B. R. Flay, and M. A. Pentz. 1998. “Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes.” American Journal of Epidemiology 147(7):694-703.

Imai, Kosuke, and In Song Kim. 2019. “When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?” American Journal of Political Science 62:467-490.

Keele, Luke, and Nathan J. Kelly. 2006. “Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables.” Political Analysis 14(2):186-205.

Lancaster, Tony. 2002. “Orthogonal Parameters and Panel Data.” Review of Economic Studies 69:647-666.

Liu, Licheng, Ye Wang, Yiqing Xu. 2019. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” Working paper: Stanford University.

Mummolo, Jonathan, and Erik Peterson. 2018. “Improving the Interpretation of Fixed Effects Regression Results.” Political Science Research and Methods 6:829-835.

Neuhaus, J. M., and J. D. Kalbfleisch. 1998. “Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data. Biometrics, 54(2): 638-645.

Pickup, Mark and Vincent Hopkins. 2020. “Transformed-Likelihood Estimators for Dynamic Panel Models with a Very Small T.” Political Science Research & Methods, forthcoming.

Xu, Yiqing. 2017. “Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models.” Political Analysis 25:57-76.

Zorn, Christopher. 2001. “Generalized Estimating Equation Models for Correlated Data: A Review with Applications.” American Journal of Political Science 45(April):470-90.

Mandatory readings before course start
Hsiao, Cheng. 2007. “Panel Data Analysis — Advantages and Challenges.” Test 16:1-22.

Examination part
Students will be evaluated on two written homework assignments that will be completed during the course (20% each) and a final examination (60%). Homework assignments will typically involve a combination of simulation-based exercises and “real data” analyses, and will be completed during the evenings while the class is in session. For the final examination, students will have two alternatives:

•    “In-Class”: Complete the final examination in the afternoon of the last day of class (from roughly noon until 6:00 p.m. local time), or

•    “Take-Home”: Complete the final examination during the week following the end of the course (due date: TBA).

Additional details about the final examination will be discussed in the morning session on the first day of the course.

Supplementary aids
The exam will be a “practical examination” (see below for content). Students will be allowed access to (and encouraged to reference) all course materials, notes, help files, and other documentation in completing their exam.

Examination content
The examination will involve the application of the techniques taught in the class to one or more “live” data example(s). These will typically take the form of either (a) a replication and extension of an existing published work, or (b) an original analysis of observational data with a panel / time-series cross-sectional component. Students will be required to specify, estimate, and interpret various statistical models, to conduct and present diagnostics and robustness checks, and to give detailed justifications for their choices.

Examination relevant literature
See above. Details of the examination literature will be finalized prior to the start of class.

Prerequisites (knowledge of topic)
This course assumes no prior experience with machine learning or R, though it may be helpful to be familiar with introductory statistics and programming.

Hardware
A laptop computer is required to complete the in-class exercises.

Software
R (https://www.r-project.org/) and R Studio (https://www.rstudio.com/products/rstudio/) are available at no cost and are needed for this course.

Learning objectives
Students will leave with an understanding of the foundational machine learning methods as well as the ability to apply machine learning to their own field of work or study using the R programming language.

Course content
Machine learning, put simply, involves teaching computers to learn from experience, typically for the purpose of identifying or responding to patterns or making predictions about what may happen in the future. This course is intended to be an introduction to machine learning methods through the exploration of real-world examples. We will cover the basic math and statistical theory needed to understand and apply many of the most common machine learning techniques, but no advanced math or programming skills are required. The target audience may include social scientists or practitioners who are interested in understanding more about these methods and their applications. Students with extensive programming or statistics experience may be better served by a more theoretical course on these methods.

Structure
The course will be designed to be interactive, with ample time for hands-on practice with the Machine Learning methods. Each day will include several lectures based on a Machine Learning topic, in addition to hands-on “lab” sections to apply the learnings to new datasets (or your own data, if desired).

The approximate schedule will be as follows:

Day 1: Introducing Machine Learning with R

• How machines learn
• Using R and R Studio
• Data exploration
• k-Nearest Neighbors
• Lab sections – installing R, choosing and exploring own dataset (if desired)

Day 2: Intermediate ML Methods – Classification Models

• Quiz on Day 1 material
• Naïve Bayes
• Lab sections – practicing with kNN and Naïve Bayes

 
Day 3: Intermediate ML Methods – Numeric Prediction

• Quiz on Day 2 material
• Decision Trees and Rule Learners
• Linear Regression
• Regression trees
• Logistic regression
• Lab sections – practicing with classification and regression methods

 
Day 4: Advanced Classification Models

• Quiz on Day 3 material
• Neural Networks
• Support Vector Machines
• Random Forests
• Lab section – practice with neural networks, SVMs, and random forests

Day 5: Other ML Methods

• Quiz on Day 4 material
• Association Rules
• Hierarchical clustering
• K-Means clustering
• Model evaluation
• Lab section – practice with these methods, work on final report

Literature

Mandatory
Machine Learning with R (4th ed.) by Brett Lantz (2023). Packt Publishing

Supplementary / voluntary
None required.

Mandatory readings before course start
Please install R and R Studio on your laptop prior to the 1st class. Be sure that these are working correctly and that external packages can be installed. Instructions for doing this are in the first chapter of Machine Learning with R.

Examination part
100% of the course grade will be based on a project and final report (approximately 10 pages), to be delivered within 2-3 weeks after the course. The project is intended to demonstrate your ability to apply the course materials to a dataset of your own choosing. Students should feel free to use a project related to their career or field of study. For example, it is possible to use this opportunity to advance dissertation research or complete a task for one’s job. The exact scoring criteria for this assignment will be provided on the first day of class. This will be graded based on its use of the methods covered in class as well as making appropriate conclusions from the data. 
There will also be brief quizzes at the start of each lecture, which cover the previous day’s materials. These are ungraded and are designed to provoke thought and discussion. 

Supplementary aids
Students may reference literature and class materials as needed when writing the final project report.

Examination content
The final project report should illustrate an ability to apply machine learning methods to a new dataset, which may be on a topic of the student’s choosing. The student should explore the data and explain the methods applied. Detailed instructions will be provided on the first day of class.

Examination relevant literature
The written report is only expected to reflect knowledge of the material presented in class, which covers chapters 1 to 10 in the Machine Learning with R (4th ed.) textbook noted above.