Courses for MMSR Programme and Class H20
Master's programme in Machine Learning, Systems and Control ◄ H20 ►
Study Year 1, Academic Year 2020/21 (Mandatory Courses)
Course Code | Credits | Cycle | S.Ex. stud. | Language | Course Name | Footnote | Links | 20/21 sp1 |
20/21 sp2 |
20/21 sp3 |
20/21 sp4 |
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F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | |||||||||
FMAN20 | 7.5 | A | X | E1 | Image Analysis | KS KE U W T | 32 | 0 | 0 | 2 | 166 | |||||||||||||||||
FRTF25 | 7.5 | G2 | - | E | Introduction to Machine Learning, Systems and Control | KS KE U W T | 16 | 0 | 12 | 0 | 72 | 14 | 0 | 6 | 0 | 80 | ||||||||||||
FRTN65 | 7.5 | A | - | E | Modelling and Learning from Data | KS KE U W T | 16 | 10 | 4 | 0 | 70 | 14 | 10 | 8 | 0 | 68 | ||||||||||||
EXTQ40 | 7.5 | A | - | E1 | Introduction to Artificial Neural Networks and Deep Learning | KS KE U W T | 34 | 10 | 30 | 0 | 126 |
Study Year 1, Academic Year 2020/21 (Elective Mandatory Courses)
Course Code | Credits | Cycle | S.Ex. stud. | Language | Course Name | Footnote | Links | 20/21 sp1 |
20/21 sp2 |
20/21 sp3 |
20/21 sp4 |
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F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | |||||||||
EDAP01 | 7.5 | A | X | E | Artificial Intelligence | KS KE U W T | 28 | 0 | 0 | 0 | 170 | |||||||||||||||||
FMSN50 | 7.5 | A | X | E | Monte Carlo and Empirical Methods for Stochastic Inference | KS KE U W T | 28 | 0 | 12 | 5 | 140 | |||||||||||||||||
FRTN60 | 7.5 | A | - | E | Real-Time Systems | KS KE U W T | 34 | 22 | 12 | 0 | 132 | |||||||||||||||||
FMAN45 | 7.5 | A | - | E1 | Machine Learning | KS KE U W T | 28 | 0 | 0 | 2 | 170 | |||||||||||||||||
FRTN70 | 7.5 | A | X | E | Project in Systems, Control and Learning | KS KE U W T | 0 | 0 | 0 | 40 | 160 |
Study Year 2, Academic Year 2021/22 (Mandatory Courses)
Course Code | Credits | Cycle | S.Ex. stud. | Language | Course Name | Footnote | Links | 21/22 sp1 |
21/22 sp2 |
21/22 sp3 |
21/22 sp4 |
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F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | |||||||||
FRTN55 | 7.5 | A | X | E | Automatic Control, Advanced Course | KS KE U W T | 30 | 28 | 12 | 0 | 130 |
Elective Courses - MMSR
Course Code | Credits | Cycle | Year | From year | S.Ex. stud. | Language | Course Name | Footnote | Links | sp1 | sp2 | sp3 | sp4 | |||||||||||||||||
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F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | F | O | L | H | S | |||||||||||
EDAN70 | 7.5 | A | 1 - 20/21 | 1 | X | E1 | Project in Computer Science | KS KE U W T | 2 | 4 | 0 | 12 | 182 | |||||||||||||||||
EDAN70 | 2 | 4 | 0 | 12 | 182 | |||||||||||||||||||||||||
FMAN95 | 7.5 | A | 1 - 20/21 | 1 | X | E1 | Computer Vision | KS KE U W T | 32 | 0 | 0 | 2 | 166 | |||||||||||||||||
BMEN20 | 7.5 | A | 1 - 20/21 | 1 | X | E1 | Project Course in Signal Processing – from Idea to App | KS KE U W T | 8 | 0 | 12 | 8 | 172 | |||||||||||||||||
EDAN70 | 7.5 | A | 1 - 20/21 | 1 | X | E1 | Project in Computer Science | KS KE U W T | 2 | 4 | 0 | 12 | 182 | |||||||||||||||||
EDAN15 | 7.5 | A | 1 - 20/21 | 1 | X | E | Design of Embedded Systems | KS KE U W T | 24 | 4 | 14 | 0 | 150 | |||||||||||||||||
EDAN40 | 7.5 | A | 1 - 20/21 | 1 | X | E | Functional Programming | KS KE U W T | 28 | 6 | 0 | 0 | 166 | |||||||||||||||||
EITN45 | 7.5 | A | 1 - 20/21 | 1 | X | E | Information Theory | KS KE U W T | 26 | 14 | 0 | 0 | 160 | |||||||||||||||||
FMSN30 | 7.5 | A | 1 - 20/21 | 1 | X | E | Linear and Logistic Regression | KS KE U W T | 24 | 0 | 26 | 2 | 120 | |||||||||||||||||
FRTN30 | 7.5 | A | 1 - 20/21 | 1 | X | E | Network Dynamics | KS KE U W T | 28 | 28 | 16 | 0 | 130 | |||||||||||||||||
EDAN70 | 7.5 | A | 1 - 20/21 | 1 | X | E1 | Project in Computer Science | KS KE U W T | 2 | 4 | 0 | 12 | 182 | |||||||||||||||||
FRTN15 | 7.5 | A | 1 - 20/21 | 1 | X | E | Predictive Control | X | KS KE U W T | Course on hold | ||||||||||||||||||||
FMNN25 | 7.5 | A | 2 - 21/22 | 2 | X | E1 | Advanced Course in Numerical Algorithms with Python/SciPy | KS KE U W T | 28 | 0 | 0 | 3 | 169 | |||||||||||||||||
FRTF20 | 7.5 | G2 | 2 - 21/22 | 2 | X | E | Applied Robotics | KS KE U W T | 28 | 22 | 8 | 20 | 100 | |||||||||||||||||
EDAF80 | 7.5 | G2 | 2 - 21/22 | 2 | X | E | Computer Graphics | KS KE U W T | 26 | 0 | 10 | 0 | 160 | |||||||||||||||||
EITG05 | 7.5 | G2 | 2 - 21/22 | 2 | X | E | Digital Communications | KS KE U W T | 24 | 28 | 4 | 0 | 144 | |||||||||||||||||
INTN01 | 7.5 | A | 2 - 21/22 | 2 | X | E | Innovation Engineering | KS KE U W T | 80 | 4 | 0 | 15 | 100 | |||||||||||||||||
EDAP20 | 7.5 | A | 2 - 21/22 | 2 | X | E | Intelligent Autonomous Systems | KS KE U W T | 24 | 0 | 30 | 0 | 144 | |||||||||||||||||
EDAN20 | 7.5 | A | 2 - 21/22 | 2 | X | E | Language Technology | KS KE U W T | 20 | 0 | 14 | 0 | 160 | |||||||||||||||||
FMSF15 | 7.5 | G2 | 2 - 21/22 | 2 | X | E | Markov Processes | X | KS KE U W T | 26 | 16 | 6 | 0 | 140 | ||||||||||||||||
FMNN01 | 7.5 | A | 2 - 21/22 | 2 | X | E | Numerical Linear Algebra | KS KE U W T | 36 | 0 | 0 | 6 | 160 | |||||||||||||||||
FRTN50 | 7.5 | A | 2 - 21/22 | 2 | X | E | Optimization for Learning | KS KE U W T | 28 | 28 | 0 | 10 | 130 | |||||||||||||||||
FMSF10 | 7.5 | G2 | 2 - 21/22 | 2 | X | E | Stationary Stochastic Processes | X | KS KE U W T | 22 | 16 | 6 | 0 | 145 | ||||||||||||||||
FMAN80 | 7.5 | A | 2 - 21/22 | 2 | X | E1 | Functional Analysis and Harmonic Analysis | KS KE U W T | 20 | 10 | 0 | 0 | 108 | 8 | 4 | 0 | 0 | 50 | ||||||||||||
FMAN15 | 7.5 | A | 2 - 21/22 | 2 | X | E | Nonlinear Dynamical Systems | KS KE U W T | 16 | 6 | 0 | 0 | 78 | 14 | 8 | 0 | 0 | 78 | ||||||||||||
EDAN95 | 7.5 | A | 2 - 21/22 | 2 | - | E | Applied Machine Learning | KS KE U W T | 28 | 0 | 14 | 0 | 156 | |||||||||||||||||
EITN70 | 7.5 | A | 2 - 21/22 | 2 | X | E | Channel Coding for Reliable Communication | KS KE U W T | 28 | 14 | 0 | 2 | 156 | |||||||||||||||||
EDAN01 | 7.5 | A | 2 - 21/22 | 2 | X | E1 | Constraint Programming | KS KE U W T | 20 | 0 | 12 | 0 | 160 | |||||||||||||||||
EDIN01 | 7.5 | A | 2 - 21/22 | 2 | X | E1 | Cryptography | KS KE U W T | 36 | 14 | 0 | 2 | 148 | |||||||||||||||||
FMSN45 | 7.5 | A | 2 - 21/22 | 2 | X | E | Mathematical Statistics, Time Series Analysis | X | KS KE U W T | 24 | 12 | 12 | 5 | 120 | ||||||||||||||||
FMAN30 | 7.5 | A | 2 - 21/22 | 2 | X | E1 | Medical Image Analysis | KS KE U W T | 32 | 0 | 0 | 3 | 165 | |||||||||||||||||
FRTN05 | 7.5 | A | 2 - 21/22 | 2 | X | E | Non-Linear Control and Servo Systems | KS KE U W T | 28 | 28 | 12 | 0 | 130 | |||||||||||||||||
FRTN40 | 7.5 | A | 2 - 21/22 | 2 | X | E | Project in Automatic Control | KS KE U W T | 0 | 0 | 0 | 40 | 160 | |||||||||||||||||
BMEN15 | 7.5 | A | 2 - 21/22 | 2 | X | E | Signal Separation - Independent Components | KS KE U W T | 14 | 28 | 8 | 0 | 150 | |||||||||||||||||
FMSN20 | 7.5 | A | 2 - 21/22 | 2 | X | E | Spatial Statistics with Image Analysis | X | KS KE U W T | 26 | 0 | 18 | 5 | 150 |
FRTN15 Predictive Control: The course is cancelled in the academic year 2020/21 but is planned to be given in 2021/22.
FMSF15 Markov Processes: The course is to be studied together with MASC03.
FMSF10 Stationary Stochastic Processes: The course is to be studied together with MASC04
FMSN45 Mathematical Statistics, Time Series Analysis: The course is to be studied together with MASM17.
FMSN20 Spatial Statistics with Image Analysis: The course is to be studied together with MASM25
Degree Projects - MMSR
The list contains the degree project courses that are included in the MMSR programme.
Course Code | Credits | Course Name | Links |
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FRTM05 | 30 | Degree Project in Automatic Control | KS KE U |
EDAM01 | 30 | Degree Project in Computer Sciences | KS KE U |
EITM02 | 30 | Degree Project in Electrical and Information Technology | KS KE U W |
FMSM05 | 30 | Degree Project in Mathematical Statistics | KS KE U |
FMAM02 | 30 | Degree Project in Mathematics | KS KE U W |