Information for incoming students in 4EU+ Master AI Network (4EUMAIN)
We welcome students from our partner 4EU+ universities to join our courses and optionally work with us on master thesis topics. The student can select almost any course from our offer of courses (link), but not all of them are taught in English every year so it is better to check the current situation. We preselected some courses that might be of interest for 4EUMAIN students (see below), the courses in our master programs in Computer Science can be found at their respective web pages.
Are you already enrolled in the master's program at Charles University? Then this page is not for you — please see our Outgoing Students page instead.
Why come to Prague?
The Czech Republic is a vibrant country with rich culture and history. Prague is one of the world’s most beautiful and safest cities that is easy to reach and transport inside. Charles University is one the oldest universities and among the world top universities. Some famous projects (NetBeans, BIRD, Ozobot, …) originated in the School of Computer Science that is hosting 4EUMAIN students. Among other AI and CS topics we offer the following research areas:
- planning, search, and optimisation (planning, constraint programming, multi-agent systems, games theory)
- machine learning and data mining (deep learning, reinforcement learning, evolutionary algorithms, neural networks)
- robotics (foundations of robotics, mobile robotics, probabilistic robotics, digital image processing)
- natural language processing (machine translation, dialogue systems, large language models)
Studies in Prague
Students enroll to the courses at the beginning of semester, enrollment is mandatory to complete the course. The courses are organized into lectures and practicals, the conditions to complete the course are announced by the teacher at the beginning of semester. Typically, the practicals require continual engagement with homeworks while exams are done at the examination period at the end of semester. The exam dates are announced prior to the examination period, each student has three tries to do the exam. Teaching is typically happening at two campuses of the university, Malá Strana and Troja.
Explanation of course structure:
M/N C+Ex: M*45 minutes lectures (typically one block) per week, N*45 minutes practicals (typically one block) per week, finished with Credit from practicals (pass/non-pass) and Exam (Excellent – 1, Very good – 2, Good - 3, Fail - 4)
Autumn term (Winter semester)
Timeline
September electronic enrolment in courses
October – early January courses (13-14 weeks)
January – early February examination period
Offered courses
|
Code |
Course title |
ECTS |
Structure |
|
Artificial Intelligence 1 |
4 |
2/1 C+Ex |
|
|
Seminar on Artificial Intelligence 1 |
2 |
0/2 C |
|
|
Applications of Computational Intelligence Methods |
5 |
0/4 C |
|
|
Logic Programming 1 |
3 |
2/0 Ex |
|
|
Probabilistic Robotics |
5 |
2/2 C+Ex |
|
|
Probabilistic graphical models |
3 |
2/0 Ex |
|
|
Principles of Distributed Systems |
3 |
2/0 Ex |
|
|
Boolean Functions and Their Applications |
3 |
2/0 Ex |
|
|
Game Theory |
3 |
2/0 Ex |
|
|
Probabilistic Methods |
3 |
2/0 Ex |
|
|
Lambda Calculus and Functional Programming 1 |
4 |
2/1 C+Ex |
|
|
Evolutionary Algorithms 1 |
5 |
2/2 C+Ex |
|
|
Social networks and their analysis |
5 |
2/2 C+Ex |
|
|
Foundations of Robotics |
5 |
2/2 C+Ex |
|
|
Constraint Programming |
5 |
2/2 C+Ex |
|
|
Machine Learning in Computer Vision |
5 |
2/2 C+Ex |
|
|
Microcontroller Programming |
5 |
2/2 C+Ex |
|
|
3D Computer Vision |
5 |
2/2 Ex |
|
|
Digital Image Processing |
4 |
3/0 Ex |
|
|
Introduction To Computational Neuroscience I |
6 |
3/1 C+Ex |
|
|
Neural Networks |
8 |
4/2 C+Ex |
|
|
Statistical Dialog Systems |
4 |
C+Ex |
|
|
Introduction to Language Technologies |
3 |
MC |
|
|
Computational Music Processing |
3 |
Ex |
|
|
Statistical Methods in Natural Language Processing |
6 |
C+Ex |
|
|
Knowledge Graphs, Ontologies and Lexical Semantics |
4 |
C+Ex |
Spring term (Summer semester)
Timeline
February electronic enrolment in courses
Late February – May courses (13-14 weeks)
June, September examination period
Offered courses
|
Code |
Course title |
ECTS |
Structure |
|
Applications of Neural Networks Theory |
3 |
2/0 Ex |
|
|
Artificial Intelligence 2 |
3 |
2/0 Ex |
|
|
Machine Learning |
3 |
2/0 Ex |
|
|
Planning and Scheduling |
3 |
2/0 Ex |
|
|
Evolutionary Algorithms 2 |
5 |
2/2 C+Ex |
|
|
Decision procedures and SAT/SMT solvers |
5 |
2/2 C+Ex |
|
|
Internet and Classification Methods |
2 |
1/1 C+Ex |
|
|
Multiagent Systems |
5 |
2/2 C+Ex |
|
|
Machine Learning in Bioinformatics |
5 |
2/2 C+Ex |
|
|
Mobile Robotics |
3 |
1/1 MC |
|
|
Data Mining |
5 |
2/2 C+Ex |
|
|
Embedded and Real Time Systems |
5 |
2/2 C+Ex |
|
|
Computer Vision |
5 |
2/2 C+Ex |
|
|
Seminar on Artificial Intelligence 2 |
2 |
0/2 C |
|
|
Seminar on Mobile Robotics |
3 |
0/2 C |
|
|
Lambda Calculus and Functional Programming 2 |
4 |
2/1 C+Ex |
|
|
Introduction To Computational Neuroscience II |
6 |
3/1 C+Ex |
|
|
Software Engineering for Dependable Systems |
3 |
0/2 C |
|
|
Information Retrieval |
5 |
2/2 C+Ex |
|
|
Dialogue Systems |
5 |
2/2 C+Ex |
|
|
Natural Language Processing |
4 |
C+Ex |
|
|
Language Technologies in Practice |
4 |
MC |
|
|
Introduction to Machine Learning with Python |
5 |
C+Ex |
|
|
Deep Learning |
8 |
C+Ex |
|
|
Deep Reinforcement Learning |
8 |
C+Ex |
|
|
Large Language Models |
3 |
C |
|
|
World’s languages in Natural Language Processing |
5 |
2/2, C+Ex |
Administrative contact:
Mgr. Jarmila VÁGNEROVÁ
erasmus-incoming@matfyz.cuni.cz
+420 951 551 404
Teaching contact:
Prof. RNDr. Roman BARTÁK, Ph.D.
roman.bartak@matfyz.cuni.cz
Web page with general Erasmus+ information:
https://www.mff.cuni.cz/cs/studenti/zahranicni-studium/program-erasmus/incoming
Web page with our CS master programs:
https://www.mff.cuni.cz/en/students/master-of-computer-science
