### Director of the specialization

Jarno Vanhatalo

### Persons responsible for discussing the study plans

Jarno Vanhatalo

### Model study plan

The Probabilistic modelling specialisation constructs a bridge between theoretical mathematics and its applications to practical data analysis tasks. Our courses include core theoretical and practical courses in statistics and machine learning. The probabilistic modelling specialisation is especially suitable for research-oriented MSc students who would like to continue for PhD studies in statistics or machine learning as well as for students who are interested in using mathematics in challenging real world data analysis tasks.

#### Example

**Year 1, period I **(15 ECTS)

MAST31701 Probability theory I (5 cr)

MAST32001 Computational statistics I (5 cr)

DATA15001 Introduction to Artificial Intelligence (5 cr)

**Year 1, period II **(15 ECTS)

MAST31702 Probability theory II (5 cr)

MAST32002 Computational statistics II (5 cr)

MAST32006 High dimensional statistics (5 cr)

**Year 1, period III **(15 ECTS)

MAST31402 Bayesian inversion (10 cr total over 2 periods)

MAST31704 Topics in probability I (5 cr)

DATA12002 Probabilistic Models (5 cr)

**Year 1, period IV **(15 ECTS)

MAST32004 Advanced course in Bayesian statistics (5 cr)

MAST31402 Bayesian inversion (continued)

MAST31705 Topics in probability II (5 cr)

**Year 2, period I **(15 ECTS)

MAST32003 Statistical inference III (5 cr)

DATA11001 Introduction to Data Science (5 cr)

Optional course (5 cr), such as Seminar in Data Science

**Year 2, period II **(10 ECTS + starting the thesis)

DATA11003 Distributed Data Infrastructures (5 cr)

DATA20001 Deep Learning (5 cr)

MSc thesis

**Year 2, period III **(thesis)

MSc thesis

**Year 2, period IV **(thesis + 5 ECTS)

MSc thesis

[Choose either one of these:]

DATA12001 Advanced Course in Machine Learning (5 cr)

MAST32005 Spatial modelling and Bayesian inference (5 cr)