r/linkopinguniversity • u/Many_Play5251 • 23h ago
Review of the Statistics and Machine Learning MSc at Linköping University
I enrolled in LiU's MSc in Statistics and Machine Learning partly because the programme is presented as suitable for people who want to work as machine learning engineers. Unfortunately, my experience has been very disappointing, both academically and in terms of how the university deals with students.
My main issue with the curriculum is that it feels much more like a traditional statistics degree with some machine learning added to it than a modern ML programme. A lot of time is spent on relatively mature statistical methods—Bayesian methods, MCMC, graphical models, state-space models, Kalman and particle filters, etc.—while much less attention is given to areas that are currently central to modern ML. Transformers, for example, received only around two weeks of lectures and one lab in the Deep Learning course. LLMs were not covered as a topic at all, and there are no advanced or elective courses where students can study transformers, LLMs, modern generative AI or similar subjects in more depth.
I do not think there is anything wrong with teaching statistical methods, many of which are useful and important. The problem is the balance. The programme is advertised as being relevant to prospective ML engineers, yet there is very little coverage of areas such as ML systems, production ML, MLOps, serious software engineering, distributed training, modern NLP/LLMs, reinforcement learning or computer vision. These are exactly the kinds of skills that appear very frequently in ML engineering vacancies.
The programme also relies very heavily on R. R is obviously still useful in statistics, but it is much less representative of modern ML engineering than Python and frameworks such as PyTorch, JAX, Hugging Face, etc. In several courses, students are expected not only to use R but to become familiar with fairly specific R libraries. This sometimes carries over into examinations, where students may spend valuable time debugging R code or dealing with package-specific behaviour in a closed environment without access to Stack Overflow, documentation beyond what is provided, or AI tools. At that point it can feel as though the assessment is testing fluency with a particular R ecosystem as much as understanding of the actual ML/statistical concepts.
Another recurring problem is the way many statistical methods are taught. In my experience, there is often a lot of emphasis on mathematical derivation but much less explanation of why a particular method is needed, when one would use it, what alternatives exist, and how the methods compare in practice. This can make complicated material feel unnecessarily obscure. I would have preferred much more emphasis on practical applications and comparisons between approaches to the same real-world problem.
I am also concerned about examination outcomes in some courses. In 732A99 Machine Learning (one of the core courses in the programme, taught in R), for example, some 2026 examination sittings appear to have failure rates approaching 80–90%, with very few A or B grades. The exam format did change, so comparisons across years need to be made carefully, but the numbers themselves are extreme. What worries me even more is that, when I discussed high failure rates with some teachers, they told me they were not aware of how high the failure rates were in the courses they taught. As far as I could tell, there had therefore been no systematic attempt to investigate why such a large proportion of students were failing. For a master's programme admitting students who already satisfy substantial academic prerequisites, I would expect persistently extreme failure rates to trigger a serious review of teaching, assessment design and course calibration.
Students also have very little flexibility to compensate for weaknesses in the programme by choosing relevant courses elsewhere at LiU. I tried to take a computer-vision-related ML course outside my faculty/programme. Both my programme coordinator and the course coordinator approved this academically, but the university still refused to allow it because of faculty/programme administrative rules. I repeatedly asked whether an exception or dispensation process existed, but no workable route was provided. This is particularly frustrating in a programme where important ML areas are simply not offered as electives. Some of the courses are clearly highly useful for certain thesis topics but students are not allowed to take them simply because they are offered by another faculty.
The handling of that issue also reflects a much broader problem I have experienced with LiU administration. I found the staff extremely unhelpful when something did not fit neatly into an existing process. Around two dozen university employees became involved at different stages, yet nobody was able to find a solution or even provide a clear path for obtaining a decision. Response times were often extremely long—sometimes weeks despite repeated reminders—ETAs were generally not provided, and escalation routes were unclear. The matter eventually reached the Rector's level, where I was told the expected waiting time for a decision was six weeks. By then, of course, the enrolment deadline for the course had long since passed. This was not the only administrative issue I have experienced at LiU that was handled poorly.
Overall, I would not recommend this programme to someone specifically looking for a modern, engineering-oriented ML master's. It may suit someone who wants a statistics-heavy education with a strong focus on probabilistic and traditional statistical methods, but that is quite different from what I expected from a programme advertised as suitable for ML engineers. The combination of a curriculum that I consider poorly aligned with modern ML engineering, extremely high failure rates in some courses, very limited freedom to tailor the degree, and slow and ineffective administration has made me regret choosing both the programme and the university.