Applied mathematics - probabilistic modelling
Director of the specialization: Jarno Vanhatalo

Director of the specialization: Jarno Vanhatalo

Per­sons re­spons­ible for dis­cuss­ing the study plans

Jarno Vanhatalo

Mandatory core courses, at least 10 cr
MAST31701 Probability theory I (5 cr)
MAST31702 Probability theory II (5 cr)

Specialization courses, at least 10 cr from this list
MAST32004 Advanced course in Bayesian statistics (5 cr)
DATA12001 Advanced Course in Machine Learning (5 cr)
MAST32001 Computational statistics I (5 cr)
MAST32002 Computational statistics II (5 cr)
MAST32006 High dimensional statistics (5 cr)
DATA11002 Introduction to Machine Learning (5 cr)
DATA12002 Probabilistic Graphical Models (5 cr)
MAST32005 Spatial modelling and Bayesian inference (5 cr)

Other advanced courses from the list of core courses, mathematics, applied mathematics, statistics courses and/or courses from other programmes as approved in personal study plan. 

MAST30001 Master´s thesis seminar (5 cr)

MAST31000 Master´s thesis (30 cr) (the link includes grading scale and criteria) 

Other requirements, such as possibly required bachelor's level mathematics and statistics courses (the module 0-35 cr other studies) are explained here.

 

Schedule and instructions of probabilistic modelling studies

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)