You have to take 32 ECTS of advanced courses: 18 ECTS of advanced theory (so programming, math, numerical methods, etc.), 12 ECTS of advanced biology and 2 ECTS of science in perspective. In the ETH course catalog, you will find some suggestions for theory and biology. This is an open list, meaning that you can take courses at ETH, UZH or UBas for these credit points even if they're not among the suggested courses. The only condition is that your mentor must sign your study plan so you will have to make sure they agree that the classes you take are relevant to the program. Most mentors aren't very strict about this as long as you use a little common sense. Some have been known to not let students take "easy" classes as these are meant to be advanced.
Below are some suggestions for advanced courses that other CBB students have taken with their feedback. Most of these are from the open list so you should have no problem with getting these accepted by your mentor.
Theory
| Course | Info | Comment |
|---|---|---|
Probabilistic Artificial Intelligence |
| Great lecturer and self-explanatory slides. |
Information Systems for Engineers |
| Great lecturer and self-explanatory slides. |
Computational Statistics |
| Great lecturer! Attendance is assumed as the slides are minimal and do not cover the entire course content. Assignments in R are not compulsory but understanding them prepares you well for the exam. The exam contains theory questions and little bit of programming in R. The course was very nice! |
Introduction to Mathematical Optimization |
| Material relevant to the exam is quite straightforward, but the lecture gives some interesting insights into more advanced topics of mathematical optimization. Enthusiastic, young lecturer. Very good lecture notes. |
Big Data |
| The most enthusiastic lecturer ever. Topic is fascinating and very relevant to our day and age, no matter what your interests within bioinformatics are. |
Big Data for Engineers |
| This is the Big Data course but for students with a less strong background in computer science. Same lecturer. Goes a less into some of the more technical aspects but gives a great overview nonetheless. |
Causality |
| Module given by a young lecturer from the seminar in statistics. Good introduction to causal inference and causal structure learning. Some maths required but derivations from lecture are not critical for exam. Lectures are not recorded, and slides not fully self-contained, so attendance very beneficial. Exam is multiple choice. |
Data Modelling and Databases |
| Is the "same" lecture as Information Systems for Engineers but for computer scientists. The lecture is funny with a lot of memes on the slides. It goes more into depth and also considers cuncurrency and locking (not in IS4E). The exam is multiple choice and similar to the ones on the lecture website. Some old exams to study with are provided. |
High Performance Computing |
| A mostly applied approach to learning about HPC methods. The lecturer's slides are quite detailed, and might require a bit of review (about an hour per week), which in turn will help greatly with the exercises. The final exam was quite fair, with everyone in the class who had achieved passing marks on the homeworks passing the year that I took this course. Structure of the exam: free answer, bug hunting, knowing how certain options in the code affect its behavior, etc. It is quite a small course as well, with only about 10-15 students. |
Computational Vision |
| More of a biological than computational lecture but very interesting. The lecturer has very minimalistic slides so make sure to attend all lectures. Gives a good overview how vision works biologically, how signals can be integrated, etc. The application of this knowledge to artificial systems (e.g. ANNs) becomes clearer. With a moderate effort a rather easy course to pass or get a good grade in. |
Advanced Approaches for Population Scale Compressive Genomics |
| Very small class (we were only 4!). Great lecturer, very enthusiastic about his topic. Class is very tough to follow though, and the concepts are complicated to say the least, so I would recommend to have a strong computer science background. Oral exam - you need to understand the concepts thoroughly but not the algorithms strictly by heart. |
Introduction to Neuroinformatics Maybe not officially part of the proposed classes anymore, so check with your mentor |
| This class spans several topics, from the more physical aspects of neurons to their biology and the applications in the domain of neuroinformatics. Very variable quality depending on the lecturer (sometimes my favorite class, sometimes my least favorite). Exam was long and multiple choice (you should know everything by heart). Overall I thought it was interesting but could benefit from better lecturers on the CS part. |
Likelihood Inference
|
| This course gives a great overview over the basics of statistical inference and its mathematical foundation. Really interesting and relevant course. The teacher and especially TA's are very good at explaining. The assignments are compulsory and require some maths and statistics background knowledge but are not graded (you just have to show effort in solving them). The exam is very similar to the exercises. |
Biology
| Course | Info | Comment |
|---|---|---|
Infectious Disease Dynamics |
| Lecture given by a number of professors from D-USYS and D-BSSE. Focus is on the modelling of disease dynamics; some background information on the biology of diseases but more a "nice-to-know" than essential. Very good lectures, which were first recorded in spring 2019. There is a self-contained, well-written script. Oral exam which appears to be solely based on exercises from the lecture script. |
ImmunoEngineering |
| Great lecture; the topics are very interesting; gives a great overview if one wants to pursue studies/a career in a related field (cell therapy, immunology...). Downside: exam is very theoretical (so you have to study a lot by heart). |
Immunology I |
| I guess this is described better by biology students at ETH in the Bachelor's as it is one of their mandatory classes. I found that it gave a great understanding of the matter. The exam is absolutely stupid though (you need to learn absolutely everything by heart for a multiple choice). |
Immunology II |
| I don't think you should take this class if you haven't done Immunology I. I thought it was a great way to deepen my knowledge after getting all the basics in Immunology I, as it is more applied and in depth. The exam is also multiple choice. |
Current Approaches in Single Cell Analysis |
| If you have already done a lot of biology, I think this is a fairly easy class. There are a lot of different lecturers so the taught materials can be redundant. For those with less of a biology background I think it's a great way to get both an overview of common biological techniques and their applications/alternatives in the domain of single cell analysis. Also, for anybody wishing to work in microfluidics/use single cell analysis techniques I think it's a great introduction. The exam was multiple choice with free text. |
Introduction to Astrobiology
|
| The lecturer is funny and the course material is very interesting if you have a popular science-level interest in Astronomy and life on other planets. Not only biological, but also environmental aspects are considered. You have to hand in several short essays and there is a written exam with a mix of open and multiple choice questions, which is easily passed if you put some effort into studying the very complete slides. |
Microbiology I |
| Classical learn everything by heart bio class. Some old exams can be found to help prepare for the exam. |
Stem Cells: Biology and Therapeutic Manipulation |
| Prof. Schroeder presents very stimulating content and crazy applications. Each student has to present a paper (alone) and need to get a pass to be accepted at the exam. Written exam (~30 questions). |
Evolutionary Medicine |
| Relaxing lecture. Written end of semester exam (i.e. during the last week of lecture). Easy to pass, but you'll have to learn a lot by heart if your want an excellent grade. |
Computational Vision |
| The lecturer has very minimalistic slides so make sure to attend all lectures. Gives a good overview how vision works biologically, how signals can be integrated, etc. The application of this knowledge to artificial systems (e.g. ANNs) becomes clearer. With a moderate effort a rather easy course to pass or get a good grade in. |
Biological Engineering and Biotechnology |
| Many different lecturers, also from industry. Lectures focus on biopharmaceutical manufacturing. The exam has been (almost) entirely on the first 4 lectures for the last two years! Learn these 4 lectures in great detail by heart. |
Science in perspective (SiP)
You have to complete one 2 ECTS course for this part of the program. You are free to take a course that is completely unrelated to CBB. Courses from other universities can be taken, so you do not necessarily have to stick to courses from D-GESS.
| Course | Info | Comment |
|---|---|---|
Introduction to Health Economics and Policy |
| Cool to get some insight into economics related to the industry we might end up in! |
Introduction to Negotiation |
| Interesting course which mixes game theory and negotiation methods with case studies and real-life examples. |
| Maths in Politics and Law |
| Not entirely sure if this is offered in Autumn 2021 but great course by an enthusiastic young lecturer. Very good lectures, including some guest lectures by people who help(ed) with the design of the Swiss voting system. Assessment is by a pass/fail assignment to be handed in after the Christmas break (at least this is how it was in autumn 2019). |