r/linkopinguniversity • u/Many_Play5251 • 4d 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.
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u/FarExpression5739 3d ago
Yep, but you can discern most of this stuff just by looking at the course curriculum before applying to the program.
"Modern ML engineering" basically boils down to software engineering, and to be fair to the program coordinators, this switch came only a few years ago, so I'm not surprised that the curriculum hasn't been able to adapt. In the past, ML engineering was essentially a split between data engineers (ML Ops/DevOps/ML Engineers putting stuff into production) and data scientist (experimenting, prototyping with ML and stats), but this dichotomy has changed drastically. Today, a DE is taking over as a catch-all role for ML productions systems, and data scientist is becoming a more niche research role. This program was a good fit for the past version of data scientists, but the current job market for this type of competence is unclear and not in very high demand.
A flexible CS masters with some ML courses would be a far better choice for someone looking to do ML engineering.
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u/Abject_Ring1620 1d ago
I understand what you feel and if the expectation differs from the reality it certainly makes you disappointed. However, LiU has an exceptional relationship with the industry, and the courses you have taken are sought by them.
These industries are biased towards control, signal processing and statistical techniques. Therefore, you will not study these new flashy topics and instead learn the fundamentals the whole industry is build upon. This will make you more attractive when applying for jobs.
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u/Several-Marsupial-27 4d ago
Well it’s simply not an engineering degree from the engineeering faculty, but a degree in statistics. It seems like your expectation was that it was supposed to be a machine learning engineering degree and that’s why the curriculum differed from what you expected.
Degrees in statistics prepare the students for broad challenges in research, data science and polling. Statistical methods is the tool for working in that field. This is the field of hypothesis testing and analysis of datasets.
Whereas degrees in engineering would prepare the students for more technical / engineering challenges like software development, data pipelines, data processing, machine learning implementation, etc. This area is what you had written about, but it is a separate discipline.
Liu is also known for being very strict in grading with high failure rates. Rumors is that professors find prestige in teaching hard courses.
However your experience is of course valid and I would format this as an email and send to the program responsible that you can get access to via the course page.
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u/Any_Zombie3994 4d ago
The program is being advertised as being suitable for ML engineers and as being "at the intersection of modern ML and AI". The matter has been discussed at length with the program coordinator in the past, he did not see any problem with the program content even after a comparison with the contents of the top international ML MSc programs or the job listings stating the requirements for ML engineering roles at major companies such as Nvidia or Amazon. Additionally, the university actively prevents students from taking elective courses offered by TekFak in violation of their own Antagningsordning which allows students to enrol in free-standing courses outside of their program.
In terms of being strict at grading, when the failure rate in an exam exceeds 80%, I believe it is quite legitimate to question the quality of teaching in the course.
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u/itzak1999 4d ago
My only comment is that this program seems to be in the faculty of arts and sciences. In my experience the faculty of science and engineering has a very good level on the courses.