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Study plan of the Master in Artificial Intelligence (MAI)
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For general information on the Master, please refer to:
Study plan of the Master in Artificial Intelligence (MAI)
The study plan (also study programme or study curriculum) of the Master includes information on the structure of the programme.
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Study plan 2023-2025
In this master programme a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwritings to the right better than any other known method.
First Semester
ECTS Core Courses
6 3 6 3 Elective courses
12 Second Semester
ECTS Core Courses
6 6 6 6
Electives
6
Third Semester
ECTS Core Courses
6 6 Master Thesis
9 Electives
9 Fourth semester
ECTS Core Courses
3 21 Electives
6 Electives Autumn Semester
6 6 6
6 6
3
6 6 3 6 3 Electives Spring Semester
6 6 6 6 3 6 6
6 3 6 6 4 6 6
6 Changes in the study programme may occur.
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Study plan 2022-2024
In this master programme a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwritings to the right better than any other known method.
First Semester
ECTS Core Courses
6 3 6 3 Elective courses
12 Second Semester
ECTS Core Courses
6 6 6 Electives
12 Third Semester
ECTS Core Courses
6 6 9 Electives
9 Fourth semester
ECTS Core Courses
3 21 Electives
6 Electives Autumn Semester
3 6 6 6 6 6 6 3 6 3 Electives Spring Semester
Advanced Computer Architectures (SP24)
6 6 6 3 6 3 Geometric Algorithms (SP24)
6 6 3 6 6 4 6 6 Changes in the study programme may occur.
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Study plan 2021-2023
In this master's programme a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state-of-the-art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwriting to the right better than any other known method.
First Semester
ECTS Core Courses
6 3 6 3 Elective courses
12 Second Semester
ECTS Core Courses
6 6 Electives
18 Third Semester
ECTS Core Courses
6 6 9 Electives
9 Fourth semester
ECTS Core Courses
3 Data Analytics*
6 21 Electives Autumn Semester
3 6 6 3 6 6 3 6 3 Electives Spring Semester
6 6 6 6 6 3 6 6 6 6 *only for 2021/22 students (originally 1st-year course)
Changes in the study programme may occur.
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Study plan 2020-2022
Study plan of the Master in Artificial Intelligence - curriculum 2020-2022
In this master programme a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwriting to the right better than any other known method.
First Semester
ECTS Core Courses
6 3 6 3 Elective courses
12 Second Semester
ECTS Core Courses
6 6 6 Electives
12 Third Semester
ECTS Core Courses
6 3 Distributed Algorithms II - Protocols and Techniques of Blockchains
3 9 Electives
9 Fourth semester
ECTS Core Courses
6 3 21 Electives Autumn Semester
3 6 3 6 6 3 6 Electives Spring Semester
6 6 6 Effective High-Performance Computing & Data Analytics Summer School
6 6 3 6 6 6 -
Study plan 2019-2021
Study plan of the Master in Artificial Intelligence - curriculum 2019-2021
In this master programme a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwritings to the right better than any other known method.
First Semester
ECTS Core Courses
6 3 6 3 Elective courses
12 Second Semester
ECTS Core Courses
6 6 6 Electives
12 Third Semester
ECTS Core Courses
6 6 9 Electives
9 Fourth semester
ECTS Core Courses
6 3 21 Electives
6 Electives Fall Semester
3 Blockchains - Protocols and Techniques for Distributed Trust*
3 6 3 6 6 3 6 Electives Spring Semester
6 6 6 6 3 6 6 6 -
Study plan 2018-2020
Study plan of the Master in Artificial Intelligence - curriculum 2018-2020
First Semester
ECTS Core Courses
Machine Learning
6 Deep Learning Lab
3 Algorithms & Complexity
6 Numerical Algorithms
3 Elective courses
12 Second Semester
ECTS Core Courses
Data Analytics
6 Stochastic Methods
6 Robotics
6 Electives
12 Third Semester
ECTS Core Courses
6 6 6 Electives
9 Fourth semester
ECTS Core Courses
6 3 21 Electives
6 Electives Fall Semester
3 Blockchains - Protocols and Techniques for Distributed Trust
3 6 3 6 6 3 6 Electives Spring Semester
6 6 6 6 Multiscale Methods
6 3 6 6 Changes in the study plan may occurr. In case of discrepancies, or for any legal purpose, the study plan indicated by the Director of the Master or the Dean's office of the Faculty of Informatics shall prevail.
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Study plan 2017-2019
Study plan of the Master in Artificial Intelligence - curriculum 2017-2019
In this master program a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwritings to the right better than any other known method.
First semester - Autumn semester 2017/18 ECTS Core Courses Machine Learning 6 Deep Learning Lab 3 Algorithms & Complexity 6 Numerical Algorithms 3 Elective courses 12 Second semester - Spring semester 2018 ECTS Core Courses Computer Vision & Pattern Recognition 6 Data Analytics 6 Stochastic Methods 6 Robotics 6 Electives 6 Third semester - Autumn semester 2018/19 ECTS Core Courses Artificial Intelligence 6 Distributed Algorithms 6 Master Thesis 9 Electives 9 Fourth semester - Spring semester 2019 ECTS Core Courses Geometric Deep Learning 3 Master Thesis 21 Electives 6 Electives Fall semester ECTS Advanced Networking 6 Cyber-security 3 High-Performance Computing 6 Introduction to Partial Differential Equations 6 Mobile Computing 6 Simulation & Data Sciences Seminar 3 User Experience Design 6 Electives Spring semester ECTS Advanced Computer Architectures 6 Business Intelligence and Applications 6 CPS-Intelligence 6 Geometric Algorithms 6 Multiscale Methods 6 Quantum Computing 6 Software Atelier: Simulation, Data Science & Supercomputing 6 Changes in the study plan may occurr. In case of discrepancies, or for any legal purpose, the study plan indicated by the Director of the Master or the Dean's office of the Faculty of Informatics shall prevail.
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Updated on: 05 July 2024
