You have to take 40 ECTS of courses within the list of core courses. This is a closed list meaning courses that are not on the list that is on the official study plan cannot be counted as core courses. You must acquire at least 1 ECTS, i.e. do one course, in each subcategory (bioinformatics, biophysics, biosystems, big data) and you must take the CBB seminar. Here is some extra information on some of the more popular core courses.
Bioinformatics
| Course | Info | Comment |
|---|---|---|
Computational Biology |
| Great course with great lecturers. Biweekly assignments consist of programming in R, manageable for a not very experienced programmer. Many students tend to take it in their 1st semester (or 3rd). Written exam can be taken in Zurich or Basel. Other view: I found the title a bit misleading. This is mostly about Tanja Stadler's research into phylogeny, phylodynamics, not really an overview over comp. bio |
Evolutionary Dynamics |
| Interesting course. Very time consuming weekly assignments with a lot of mathematical derivations and some coding in R (which can be made easier by watching the previous year solution via the video portal). Assignments can be done in groups and derivations are a great bonding experience! Oral exam in Basel - so far Prof Beerenwinkel let student choose one out of two or three areas tested in the exam. |
Statistical Analysis of High-Throughput Genomic and Transcriptomic Data |
| One exercise to solve every week, but only the 9 best exercises count for the grade (so you can skip the last ones if you collected enough points in the first 9 weeks). Jounal Club (groups of 2-3 students have to present a paper). No final exam but a project to hand in begining of January. Prof. Robinson is easy to reach and to get feedback from using the course's Slack. Personal Opinion: Course is very advanced and does not really explain theory, you might be overwhelmed if you take it in first semester. However you don't really have to understand the topics to get a fine grade, as Homework, Journal Club and Project are graded generously. |
Biophysics
| Course | Info | Comment |
|---|---|---|
Molecular and Structural Biology I: Protein Structure and Function |
| Exam consists mostly of bio factoids, so grind 'em away! Also, pay attention to the papers and experimental approaches presented in the course -- experimental design and result interpretation will be tested. |
Molecular and Structural Biology II: From Gene to Protein |
| Typical biology course as in you need to learn everything by heart for the exam. |
Protein Biophysics |
| Lecture consists of 4 parts lectured by different professors. Some knowledge of basic biochemistry is useful, but not necessary. The exam is a written one and is manageable. One professor hands out a very nice and detailed script. |
Current Topics in Biophysics |
| It is like a Journal Club. Each week a paper is presented and it is discussed in the following week. Pass or fail (no exam). Title is slightly misleading - usually more like Current Topics in Quantitative Biology. |
Biophysical Methods |
| Overview of the biophysical methods in life sciences. Oral presentation during the course and oral exam. |
Classical Simulation of (Bio)Molecular Systems (formerly CSMS) |
| Solid, rigorous overview of molecular dynamics simulations; assorted related methods on the side. Two great lecturers, one of them positively enthusiastic. Bi-weekly guided simulation exercises to get your hands dirty. Time-consuming report on each of these (effort: ca. 2 days every 2nd week), but grading is lenient. Exam fast-paced but fair & fairly predictable, given reports from older semesters. Previous exposure to statistical thermodynamics recommended. If you ever took any physical chemistry, you'll be fine. |
Biosystems
| Course | Info | Comment |
|---|---|---|
Computational Systems Biology |
| 3 hour lecture - caffeinate profusely before and during the lecture. Attend every Exercise session and fully understand the solutions - exam will be based on them. Large overlap with Mathematical Modelling for Biotechnology and Systems Biology (MMod). More topics are covered than in MMod, but are covered in less depth than in MMod in exchange. The exercises are more superficial (and easier) than in MMod. |
Synthetic Biology I |
| Lecture with two major components: theory, ie. modelling, which is a Comp Sys Bio light. Also given by Prof Stelling. Having taken Comp Sys Bio helps! Second part is biology, ie. milestone papers and application examples, given by Prof Panke. A short presentation (10 min between 2 or 3 people) required to be allowed to sit the exam. |
Spatio-Temporal Modelling in Biology |
| Oral exam (same as below because they are from Prof. Iber). Intensive mathematical derivation. Watch prerecording and discuss in the lecture time. No script but slides are enough. |
Mathematical Modelling for Bioengineering and Systems Biology |
| Oral exam. You should be able to explain all the content and know the main formulas and the way they were derived. Large overlap with Computational Systems Biology (CSB). Goes more into mathematical depth than CSB which is nice if you really want to understand the topics, but the lecturer is then also expecting you to understand the maths, which is not really the case in CSB. |
Data science
| Course | Info | Comment |
|---|---|---|
Data Mining I |
| Great course, Prof Borgwardt teaches slowly and repeats the previous lecture in the first hour of the current one, so everyone can follow easily. |
Data Mining II |
| Logical next step after data mining I but with a stronger focus on theoretical aspects and derivations. Also biweekly (mostly) coding assignments. |
Statistical Models in Comp Bio |
| Great lecture for modern Bayesian methods. The lecture contains heavy stats and derivations, but with a good background in math the slides are self explanatory. |
Functional Genomics |
| Exam consists of multiple choice questions, lots and lots and lots of memorization needed! |
Statistical Analysis of High-Throughput Genomic and Transcriptomic Data |
| Assignments every week in R, which review the topic studied in the lecture. Not super hard, but moderately time consuming. A good knowledge of statistics can help to have a better understanding but is not essential. No exam, only a final project in which analysis from a paper is replicated or "consulting" for a real, current project is performed. |
Advanced Machine Learning Mutually exclusive with Machine Learning from UBas |
| As the title says, advanced course. Prior knowledge is key - either data mining I + II or Intro to ML (which is considered as a core course in the new study plan). Assignments are ML tasks with scoring Kaggle-style. Exam is all math/derivations |
Machine Learning Mutually exclusive with Advanced Machine Learning from ETH |
| Comparable to Krause's Intro to ML, but focuses heavily on Statistical learning theory and SVMs. |
Introduction to Machine Learning |
| IML, as its name suggests, is an introduction to machine learning. It includes supervised learning, unsupervised learning, generative modeling, neural networks, etc. It tries to give you an overview of the methods and algorithms with a concrete view over the mathematical basis of algorithms. There were 4 projects in the previous semester, with 1 easy project, and 3 project concerning a real machine learning task, which was challenging to solve, but really useful. There are several theory exercises as well, which you are not supposed to hand in. They cover several aspects of the theory and try to help you understand the course material and prepare your for the exam. An important problem of this course is that the exam takes place on the computer, so your final answers are important and this might be a bit unfair sometimes. |
CBB seminar
2 ECTS
Autumn semester in Basel / Spring semester in Zurich
Basel version will have only 2-4 students presenting so the time commitment is smaller, while Zurich version is more crowded.
ETH
This is the only mandatory course of the whole program. It basically consists in presenting one paper in some field on bioinformatics or computational biology and participating in discussions after your fellow students' presentations. Papers will be assigned at the beginning of the semester and presentations will start around the middle of the semester depending on the number of participants.