Appendix 1: Course list

Master's Programme, Machine Learning, 120 credits (TMAIM), Programme syllabus for studies starting in autumn 2018

General courses

Year 1

Conditionally elective courses

Recommended courses

Supplementary information

During year 1 and year 2 students must take at least complete 25 credits from the grouping listed in 1.2.1 and at least 13,5 credits from the group of courses in 1.2.2.

1.2.1 Conditionally Elective Courses - Application Domains

COMPUTER VISION:
DD2423 Image Analysis and Computer Vision, 7,5 hp,
DD2424 Deep learning in Data Science, 7,5 hp,
DD2429 Computational photography, 6 hp.

LANGUAGE PROCESSING: SPEECH & TEXT
DT2112 Speech Technology, 7,5 hp,
DT2119 Speech and Speaker Recognition, 7,5 hp
DD2418 Language Engineering, 6.0 hp

VISUALIZATION:
DD2257 Visualization, 7,5 hp

ROBOTICS:
DD2410 Introduction to Robotics, 7,5 credits
DD2438 Artificial Intelligence and Multi Agent Systems, 15 credits
DD2425 Robotics and Autonomous Systems, 9 credits
DD2411 Research project in Robotics, Perception, and Learning, 15 credits

DATABASES/INFORMATION RETRIEVAL:
DD2476 Search Engines and Information Retrieval Systems, 9 hp

COMPUTATIONAL BIOLOGY:
DD2435 Mathematical Modelling of Biological Systems, 9 hp,
DD2401 Neuroscience, 7,5 hp,
DD2402 Advanced Individual Course in Computational Biology, 6 hp,
DD2404 Applied Bioinformatics, 7,5 hp.

1.2.2 Conditionally Elective Courses - Theory

MATHEMATICS:
EL2320 Applied Estimation, 7,5 hp
SF1811 Optimization, 6 hp

STATISTICS & PROBABILITY:
DD2447 Statistical Methods in Applied Computer Science, 6 hp,
SF2930 Regression Analysis, 7,5 hp,
SF2943 Time Series Analysis, 7,5 hp,
SF2940 Probability theory, 7,5 hp.

MACHINE LEARNING:
EQ2341 Pattern Recognition and Machine Learning, 7,5 hp
DD2437 Artificial Neural Networks and Deep Architectures, 7,5 hp
ID2222 Data Mining 7.5
ID2223 Scalable Machine Learning and Deep Learning 7.5
DD2420 Probabilistic Graphical Models, 7,5 credits
EL2805 Reinforcement Learning, 7,5 credits

Common Elective Courses

Elective courses are selected freely from all Second cycle courses and language courses given at KTH. First cycle courses at KTH may be taken upon permission from the Programme Director. Not more than 30 ECTS credits in total can be acquired from First cycle courses.

Year 2

Conditionally elective courses

Recommended courses

Supplementary information

During year 1 and year 2 students must take at least complete 25 credits from the grouping listed in 1.2.1 and at least 13,5 credits from the group of courses in 1.2.2.

1.2.1 Conditionally Elective Courses - Application Domains

COMPUTER VISION:
DD2423 Image Analysis and Computer Vision, 7,5 hp,
DD2424 Deep learning in Data Science, 7,5 hp,
DD2429 Computational photography, 6 hp.

LANGUAGE PROCESSING: SPEECH & TEXT
DT2112 Speech Technology, 7,5 hp,
DT2119 Speech and Speaker Recognition, 7,5 hp
DD2418 Language Engineering, 6.0 hp

VISUALIZATION:
DD2257 Visualization, 7,5 hp

ROBOTICS:
DD2410 Introduction to Robotics, 7,5 credits
DD2438 Artificial Intelligence and Multi Agent Systems, 15 hp
DD2425 Robotics and Autonomous Systems, 9 hp
DD2411 Research project in Robotics, Perception, and Learning, 15 credits

DATABASES/INFORMATION RETRIEVAL:
DD2476 Search Engines and Information Retrieval Systems, 9 hp

COMPUTATIONAL BIOLOGY:
DD2435 Mathematical Modelling of Biological Systems, 9 hp,
DD2401 Neuroscience, 7,5 hp,
DD2402 Advanced Individual Course in Computational Biology, 6 hp,
DD2404 Applied Bioinformatics, 7,5 hp.

1.2.2 Conditionally Elective Courses - Theory

MATHEMATICS:
EL2320 Applied Estimation, 7,5 hp
SF1811 Optimization, 6 hp

STATISTICS & PROBABILITY:
DD2447 Statistical Methods in Applied Computer Science, 6 hp,
SF2930 Regression Analysis, 7,5 hp,
SF2943 Time Series Analysis, 7,5 hp,
SF2940 Probability theory, 7,5 hp.

MACHINE LEARNING:
EQ2340 Pattern Recognition 7,5 hp
DD2437 Artificial Neural Networks and Deep Architectures, 7,5 hp
ID2222 Data Mining 7.5
ID2223 Scalable Machine Learning and Deep Learning 7.5
DD2420 Probabilistic Graphical Models, 7,5 credits
EL2805 Reinforcement Learning, 7,5 credits

Common Elective Courses

Elective courses are selected freely from all Second cycle courses and language courses given at KTH. First cycle courses at KTH may be taken upon permission from the Programme Director. Not more than 30 ECTS credits in total can be acquired from First cycle courses.