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Block schedule »   Study programme syllabus for class 2021/2022 »  | Year:  1  2 
Study programme for  
MPDSC - DATA SCIENCE AND AI, MSC PROGR Academic year: 2021/2022
DATA SCIENCE OCH AI, MASTERPROGRAM
The Study programme is adopted 2019-02-21 by Dean of Education
First Year
Explanations
Dept Course code Module
code		 Note Block Course name, Module type Extent/
period		 Regular
exam		 Re-sit examination
Oct -21 up to Aug -22
AUTUMN TERM
Study period 1
Compulsory courses
37 DAT405 0119 E Introduction to data science and AI, Written and oral assignments 7,5
11 TMA947 0103  Nonlinear optimisation, Laboratory 1,5
11 TMA947 0203 S Nonlinear optimisation, Examination 6,0 28/10-2021 am J
Elective courses
37 DAT246 0114  A Empirical software engineering, Written and oral assignments 2,5
37 DAT246 0214 S A Empirical software engineering, Examination 5,0 25/10-2021 pm J 05/01-2022 am J 23/08-2022 pm J
37 DAT465 0121 E 1)  A Causality and causal inference, Written and oral assignments 7,5
30 FFR105 0199 E D Stochastic optimization algorithms, Examination 7,5 27/10-2021 pm J 03/01-2022 am J 25/08-2022 am J
16 FFR135 0100 E B+ Artificial neural networks, Examination 7,5 25/10-2021 am L 04/01-2022 pm J 18/08-2022 pm J
11 MVE187 0117  Computational methods for Bayesian statistics, Project 2,0
11 MVE187 0217 S Computational methods for Bayesian statistics, Examination 5,5 30/10-2021 am J
70 RRY025 0107 E 1) 2) 3)  C+ Image processing, Examination 7,5 28/10-2021 pm J 04/01-2022 pm J 19/08-2022 pm J
32 SSY340 0117  A Deep machine learning, Project 3,0
32 SSY340 0217 S A Deep machine learning, Written and oral assignments 4,5
45 TEK650 0120 S D Strategic management of technological innovation , Examination 7,5 27/10-2021 pm J 05/01-2022 pm L 17/08-2022 am J
37 TIN093 0114 E 4)  A Algorithms, Examination 7,5 23/10-2021 pm L 25/08-2022 pm J
11 TMA265 0101 E Numerical linear algebra, Examination 7,5 26/10-2021 pm J
11 TMA881 0101 E 1)  High performance computing, Examination 7,5 Contact examiner Contact examiner Contact examiner
Study period 2
Compulsory courses
11 MVE550 0118  B Stochastic processes and Bayesian inference, Examination 6,0 08/01-2022 am J 13/04-2022 am J 22/08-2022 am J
11 MVE550 0218 S B Stochastic processes and Bayesian inference, Written and oral assignments 1,5
Elective courses
37 DAT450 0120 S 1) 3)  A Machine learning for natural language processing, Written and oral assignments 7,5
32 EEN020 0118  C Computer vision, Project 3,0
32 EEN020 0218 S C Computer vision, Written and oral assignments 4,5
11 MVE095 0106 E 4)  Options and mathematics, Examination 7,5 13/01-2022 am J
11 MVE172 0120  1)  Basic stochastic processes and financial applications, Laboratory 3,0
11 MVE172 0220 S 1)  Basic stochastic processes and financial applications, Examination 4,5 04/12-2021 am J
11 MVE190 0108 E Linear statistical models, Examination 7,5 11/01-2022 pm J
32 SSY130 0107 S D Applied signal processing, Examination 7,5 12/01-2022 pm J 11/04-2022 pm J 23/08-2022 pm J
32 SSY316 0120 E 1)  B Advanced probabilistic machine learning, Project 7,5
37 TDA251 0107 E C Algorithms, advanced course, Project 7,5
37 TDA357 0106  4)  D+ Databases, Examination 4,5 12/01-2022 pm J 25/08-2022 pm J
37 TDA357 0206 S 4)  D+ Databases, Laboratory 3,0
37 TDA507 0113 E A Computational methods in bioinformatics, Written and oral assignments 7,5
37 TDA596 0107  C Distributed systems, Examination 6,0 11/01-2022 am J 12/04-2022 pm J 17/08-2022 am J
37 TDA596 0207 S C Distributed systems, Laboratory 1,5
45 TEK656 0121 E Creating technology-based ventures, Examination 7,5 10/01-2022 am J 11/04-2022 am J 22/08-2022 pm J
11 TMA521 0197 S 1)  Large scale optimization, Examination 7,5 14/01-2022 pm J Contact examiner
 
SPRING TERM
Study period 3
Compulsory courses
37 DAT410 0119 E D Design of AI systems, Written and oral assignments 7,5
Elective courses
37 DAT340 0117  4) 5)  B Applied Machine Learning, Examination 4,0
37 DAT340 0217 S 4) 5)  B Applied Machine Learning, Written and oral assignments 3,5
32 SSY098 0119  1) 2)  C Image analysis, Project 3,5
32 SSY098 0219 S 1) 2)  C Image analysis, Laboratory 4,0
37 TDA233 0120  4) 5)  B Algorithms for machine learning and inference, Written and oral assignments 3,0
37 TDA233 0220 S 4) 5)  B Algorithms for machine learning and inference, Examination 4,5
37 TDA357 0106  4)  Databases, Examination 4,5
37 TDA357 0206 S 4)  Databases, Laboratory 3,0
70 TIF150 0107 E B+ Information theory for complex systems, Examination 7,5
37 TIN093 0114 E 4)  A+ Algorithms, Examination 7,5
Study period 4
Elective courses
37 DAT340 0117  4)  C Applied Machine Learning, Examination 4,0 Contact examiner
37 DAT340 0217 S 4)  C Applied Machine Learning, Written and oral assignments 3,5
37 DAT440 0120  1)  B Advanced topics in machine learning, Written and oral assignments 3,5
37 DAT440 0220 S 1)  B Advanced topics in machine learning, Examination 4,0
37 DAT470 0121  4)  Computational techniques for large-scale data, Written and oral assignments 4,5
37 DAT470 0221 S 4)  Computational techniques for large-scale data, Examination 3,0
37 DAT475 0121  4)  Advanced databases, Written and oral assignments 3,0
37 DAT475 0221 S 4)  Advanced databases, Examination 4,5
11 MVE165 0107 S Linear and integer optimization with applications, Examination 7,5
11 MVE441 0120  4)  Statistical learning for big data, Project 1,5
11 MVE441 0220 S 4)  Statistical learning for big data, Take-home examination 6,0
32 SSY115 0107 E B Health Informatics, Project 7,5
11 TMS016 0101 S 1)  Spatial statistics and image analysis, Examination 7,5
11 TMS088 0117 E 1)  Financial time series, Examination 7,5
1) Compulsory elective: Compulsory elective course. (DAT440, DAT450, DAT465, MVE172, RRY025, SSY098, SSY316, TMA521, TMA881, TMS016, TMS088): 2 of stated courses are required for the degree
2) Overlap: Only one of the marked courses can be included in the degree (RRY025, SSY098)
3) Recommendation: The course is normally followed during the second year of the Masters programme (DAT450, RRY025)
4) Compulsory elective: Compulsory elective course. (DAT340, DAT470, DAT475, MVE095, MVE441, TDA233, TDA357, TIN093): 2 of stated courses are required for the degree
5) Overlap: Only one of the marked courses can be included in the degree (DAT340, TDA233)
* Element includes education in another quarter
S Final grade. All module grades are reported before the final grade for the course can be reported.
E The only module in the course. Module grade and grade for the course are reported at the same time.
DIG Digital examination is a examination written in the Inspera system. The student will bring their own computer and access the exam via Safe exam browser
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