All the fields of full-time and combined study modes are displayed for the faculty selected. The system processes the information collected over the last 4 terms. For each field of study, the System selects all the programmes of study in which the field was active on the day the teaching period started in the term. Only the programmes which started with proper enrolment are taken into account (those which started with a student migrating from another programme and the ones with more than two courses recognized, etc., are being ignored). The System takes into account the enrolment in the courses which are included in grade average calculations, offered within regular terms, and not recognized. For every enrolment instance, the System finds out how many fields were active in the programme of study on the day the teaching period started for the term. If it is only one, the enrolment instance is included in the calculation for the given field. If the programme has more than one active field, the System tries to find out whether there is any field associated with the enrolment instance. If this is the case, the field is used, and if not, the System searches for the fields the course is directly associated with. The course is included in the calculation only for those fields of the programme to which the course is directly associated with. If none of the fields the course is directly associated with are the active fields on the day the teaching period starts for the term, the enrolment instance is included in the calculation for all the active fields of the programme of study. The term used is the one the Office for Studies specifies for the active field as valid as of the start of teaching period in the term. The percentage gives the ratio of all the programmes of study having the field as active in the term on the day the teaching period starts to the same programmes of study with the course enrolled in. Only the first enrolment in the course within the programme is being taken into account; repeated enrolments are ignored (i.e. in the situations where the student failed to complete the course before and thus re-enrolled in it). The enrolment instances with the percentage lower than 10 are being ignored. The number of terms displayed corresponds to the standard length of studies.

Bioinformatics and systems biology (master's full-time specialized)

1st year
1st term
FI:PB050 Modelling and Prediction in Systems Biology • 86 %
FI:PV290 Chemoinformatics • 86 %
FI:IV108 Bioinformatics II • 79 %
FI:IV126 Fundamentals of Artificial Intelligence • 79 %
FI:PV251 Visualization • 79 %
FI:MA012 Statistics II • 57 %
FI:PA054 Formal Methods in Systems Biology • 21 %
FI:PA152 Efficient Use of Database Systems • 21 %
FI:PB172 Systems Biology Seminar • 21 %
FI:PV021 Neural Networks • 21 %
FI:PV056 Machine Learning and Data Mining • 21 %
FI:PV211 Introduction to Information Retrieval • 21 %
FI:DUCIT Teaching Lab • 14 %
FI:IV120 Continuous and Hybrid Systems • 14 %
FI:PA039 Supercomputer Architecture and Intensive Computations • 14 %
2nd term
FI:PA039 Supercomputer Architecture and Intensive Computations • 77 %
FI:PV056 Machine Learning and Data Mining • 69 %
FI:PA054 Formal Methods in Systems Biology • 62 %
FI:PA152 Efficient Use of Database Systems • 62 %
FI:PV211 Introduction to Information Retrieval • 54 %
FI:PB172 Systems Biology Seminar • 46 %
FI:IV106 Bioinformatics seminar • 38 %
FI:PV269 Advanced methods in bioinformatics • 23 %
FI:PV270 Biocomputing • 15 %
FI:PV287 Artificial Intelligence and Machine Learning in Healthcare • 15 %
2nd year
3rd term
FI:PA183 Project in Systems Biology • 73 %
FI:IV120 Continuous and Hybrid Systems • 64 %
FI:IV110 Project in Sequence Analysis • 55 %
FI:PV225 Laboratory of Systems Biology • 55 %
FI:PV021 Neural Networks • 36 %
FI:PV251 Visualization • 27 %
FI:SDIPR Diploma Thesis • 27 %
PřF:JNP03 German for Science 3 • 18 %
FI:IV108 Bioinformatics II • 18 %
FI:PB172 Systems Biology Seminar • 18 %
FI:PV290 Chemoinformatics • 18 %
4th term
FI:PV269 Advanced methods in bioinformatics • 40 %
FI:SDIPR Diploma Thesis • 40 %
FI:IV106 Bioinformatics seminar • 30 %
FI:PA228 Machine Learning in Image Processing • 20 %
FI:PV211 Introduction to Information Retrieval • 20 %
FI:PV287 Artificial Intelligence and Machine Learning in Healthcare • 20 %
PdF:ONLINE_A English Online • 20 %

The information was preprocessed: 30/11/2024 11:32

All the fields of the faculty