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.

Machine learning and artificial intelligence (master's full-time specialized)

1st year
1st term
FI:IV111 Probability in Computer Science • 56 %
FI:IV126 Fundamentals of Artificial Intelligence • 56 %
FI:MA012 Statistics II • 56 %
FI:PA163 Constraint programming • 50 %
FI:PV251 Visualization • 50 %
FI:PA152 Efficient Use of Database Systems • 36 %
FI:PV056 Machine Learning and Data Mining • 33 %
FI:IA008 Computational Logic • 28 %
FI:PA039 Supercomputer Architecture and Intensive Computations • 25 %
FI:PV211 Introduction to Information Retrieval • 25 %
FI:PV021 Neural Networks • 22 %
FI:PV254 Recommender Systems • 19 %
FI:PA153 Natural Language Processing • 14 %
FI:PV027 Optimization • 14 %
FI:MA018 Numerical Methods • 11 %
PdF:ONLINE_A English Online • 11 %
2nd term
FI:PA039 Supercomputer Architecture and Intensive Computations • 73 %
FI:IA008 Computational Logic • 69 %
FI:PA152 Efficient Use of Database Systems • 69 %
FI:PV056 Machine Learning and Data Mining • 65 %
FI:PV211 Introduction to Information Retrieval • 62 %
FI:PV254 Recommender Systems • 35 %
FI:PA228 Machine Learning in Image Processing • 31 %
FI:PV027 Optimization • 31 %
FI:PA212 Advanced Search Techniques for Large Scale Data Analytics • 23 %
FI:MA012 Statistics II • 19 %
FI:PA026 Artificial Intelligence Project • 19 %
FI:IV126 Fundamentals of Artificial Intelligence • 15 %
FI:PA163 Constraint programming • 15 %
FI:IV111 Probability in Computer Science • 12 %
FI:PV021 Neural Networks • 12 %
FI:PV251 Visualization • 12 %
2nd year
3rd term
FI:PA153 Natural Language Processing • 52 %
FI:PV021 Neural Networks • 43 %
FI:IV127 Adaptive Learning Seminar • 30 %
FI:PV027 Optimization • 30 %
FI:PV115 Laboratory of Knowledge Discovery • 30 %
FI:IV126 Fundamentals of Artificial Intelligence • 26 %
FI:PA228 Machine Learning in Image Processing • 26 %
FI:IA168 Algorithmic game theory • 22 %
FI:PA163 Constraint programming • 22 %
FF:DSBCB46 The Role of the Dog and the Elephant in Antiquity • 13 %
FI:IV111 Probability in Computer Science • 13 %
FI:IV125 Formela lab seminar • 13 %
FI:MA012 Statistics II • 13 %
FI:PA164 Machine learning and natural language processing • 13 %
FI:PV251 Visualization • 13 %
4th term
FI:PA228 Machine Learning in Image Processing • 56 %
FI:SDIPR Diploma Thesis • 44 %
FI:PA026 Artificial Intelligence Project • 31 %
FI:IA008 Computational Logic • 25 %
FI:PA128 Similarity Searching in Multimedia Data • 19 %
FI:PV027 Optimization • 19 %
FI:PV056 Machine Learning and Data Mining • 19 %
FI:PV115 Laboratory of Knowledge Discovery • 19 %
FI:PV254 Recommender Systems • 19 %
FI:SOBHA Defence of Thesis • 19 %
FI:SZMGR State Exam (MSc degree) • 19 %
FI:IB109 Design and Implementation of Parallel Systems • 13 %
FI:IV127 Adaptive Learning Seminar • 13 %
FI:PB176 Basics of Quality and Managment of Source Code • 13 %
FI:PV251 Visualization • 13 %
PdF:CORE071 Practical psychology and communication • 13 %
ESF:CORE011 Economic style of thinking • 13 %

The information was preprocessed: 29/6/2024 11:29

All the fields of the faculty