This page includes information about aim and learning outcomes, teachers, literature, projects, examination, examination procedures, and course evaluation. The program for all teaching sessions can be found on a separate page (Course syllabus).

Aim and learning outcomes

The aim and learning outcomes of the course can be found in the course plan.

Changes compared to the last occasion

Projects are not part of the home exam any longer

Teachers

Course coordinator: Felix Held (felix.held@chalmers.se)

Examiner: Rebecka Jörnsten (jornsten@chalmers.se)

Teaching assistants:

Juan Inda Diaz (inda@chalmers.se)
Sebastian Persson (sebpe@chalmers.se)

Course literature

Most of the course content can be found in

Hastie T, Tibshirani R, and Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer Science+Business Media, LLC

This book is freely available online. If I refer to ESL, then I mean this book.

Other helpful books

Books that have a more practical angle than ESL:
- James G, Witten D, Hastie T, and Tibshirani R (2013) An Introduction to Statistical Learning: With Applications in R. New York: Springer Science+Business Media, LLC
  The little sibling of ESL, freely available online.
- Kuhn M and Johnson K (2013) Applied Predictive Modeling. New York: Springer Science+Business Media, LLC
For a theoretical angle on the more traditional (small to medium data) parts of the course:
- Falk M, Marohn F, and Tewes B (2002) Foundations of Statistical Analyses and Applications with SAS. Basel: Birkhäuser
A very pedagogic book with a (mostly) Bayesian angle
- Bishop CM (2006) Pattern Recognition and Machine Learning. New York: Springer
A compendium and also more focus on the Bayesian angle. Tougher for learning but great for reference
- Murphy KP (2012) Machine Learning: A Probabilistic Perspective. Cambridge, Massachusetts: The MIT Press
For the parts of the course concerning sparse modelling and penalisation methods the following book can be useful
- Hastie T, Tibshirani R, and Wainwright M (2016) Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press
  This book is freely available online.

Projects

Project work will be part of this course.

Rules

You will work on four projects during the course.
There will be multiple topics for the projects and you are free to choose one (up to a maximum number of people on the same topic).
You can work alone or in a small group (maximum 3 people) on the projects for about one week.
Once done, you upload a short summary presentation of your work (results, insights, ...) to Canvas and you indicate who you worked with.
There will be four discussion classes where you get randomly matched with others who worked on the same topic. We want you to discuss your results in light of others' results and finally to create a small summary presentation highlighting the most important insights/findings. This presentation is uploaded to Canvas.
Successful completion of all steps above will make you eligible for the home exam.

The project discussion sessions are planned to be on the following dates

3. April
24. April
8. May
15. May

during the normal course hours from 13.15 to 15.00.

Resources

You are free in choice of programming language (R, Python, Julia, Matlab, ...). Currently, R and Python are the most common in the data science community and most packages will be available for these languages.

Depending on the project topic it can be necessary to find real data sets to work with. Below is a list of some websites where you can find data. The terms interesting or awesome below are part of the names of the websites, not a value judgement.

Examination

The course is examined with a home exam. You will receive data analysis tasks that you need to solve with your knowledge from the course. The home exam will be distributed on the 28. May after the lecture and the deadline for submission is on the 12. June.

It is not meant that you spend two weeks on the home exam. However, since the home exam period overlaps with the normal exam period, we want to give you flexibility concerning when you work on the home exam.

Examination procedures

In Chalmers Student Portal you can read about when exams are given and what rules apply to exams at Chalmers. In addition to that, there is a schedule when exams are given for courses at the University of Gothenburg.

Everybody fulfilling the basic criteria (participated in all projects) during the course is automatically registered to the exam in June 2020. The exercises for the home exam are sent out to everybody who is registered.

After the exam has been graded, you can see your results in Ladok.

Re-examination

A re-exam is offered during the re-exam period at the end of August. To register, send an email to the course coordinator (felix.held@chalmers.se). Other possibilities for re-exams are offered at the beginning of January 2021 or the exam period around easter 2021 if at least 5 students are interested.

Feedback on graded exams

Feedback on the graded exam can be requested by writing an email to the course coordinator (felix.held@chalmers.se) once all grading has been finished and the grades have been submitted to Ladok.

Course evaluation

At the beginning of the course, at least two student representatives should have been appointed to carry out the course evaluation together with the teachers. The evaluation takes place through conversations between teachers and student representatives during the course and at a meeting after the end of the course when the survey result is discussed and a report is written.

Guidelines for Course evaluation in Chalmers student portal.

Student representatives

The following students have been appointed:

Program

The program can be found on the Course syllabus.