Ph.D at Abo Akademi University in Finland. Teaching & research positions in Sweden: at Lulea University of Technology, Linkoping University, KTH Royal Institute of Technology (current position). Visiting researcher at University of North Carolina at Chapel Hill, Louisiana State University at Baton Rouge, UTIA of Czech Academy of Sciences, University of Warsaw, Institute of Advanced Study at Aalto University, Visiting lecturer and researcher at University of Helsinki, University of Turku, University of Makerere. Author (co-author) of three monographs.

Has worked in signal processing, biomathematics and genetics groups.

1. Computational skill in probability: Axioms of Probability; Distributions of probability theory; Conditional probability and Expectation; Martingales; Characteristic functions; Multivariate Gaussian; Probability generating functions; Convergence concepts of probability theory; Law of large numbers; Central limit theorem; Stationary stochastic processes; Wiener process; Ornstein-Uhlenbeck Process; Poisson process.

2. Application of A in theory of statistical inference and machine Learning: PAC-theory; Expected risk minimisation; Exponential families of distributions; Bayesian theory; Maximum Likelihood; Bias-Variance Decomposition of data science; Confidence intervals; Model choice in machine learning; Expectation maximisation algorithm; High-dimensional data.

**SKILLS:**

- Statistics

- Applied Mathematics

- Matlab

- Machine Learning

**DATE: ****1**8 Feb - 8 Mar, 2019

**DURATION: **3 Week

**LECTURES: **3 Hours per day

**LANGUAGE: **English

**LOCATION: **Barcelona, Harbour.Space Campus

**COURSE TYPE: **Offline

WHAT YOUWILL LEARN

ABOUT TIMO

**HARBOUR.SPACE **

The aim of the course is to introduce basic theories and methods of pure probability theory at an intermediate level and their applications to chosen topics of theory of statistical inference and machine learning. No knowledge of measure and integration theory is required, and only bare first statements of that will be included in the course.

Techniques developed in this course are important in AI, time series analysis, financial analysis, signal processing, econometrics, and other branches of engineering and science. The course gives also a background and motivation for studies of advanced courses in probability and statistics.

TIMOKOSKI

HARBOUR.SPACEUNIVERSITY

**DATE: **18 Feb** - **8 Mar, 2019

**DURATION: **3 Weeks

**LECTURES: **3 Hours per day

**LANGUAGE: **English

**LOCATION: **Barcelona, Harbour.Space Campus

**COURSE TYPE: **Offline

All rights reserved. 2017

COURSE OUTLINE

**Session 1**

Sigma-fields,

Probability space.

Axioms of probability calculus

**Session 2**

Some Theorems of Probability calculus.

Probability on propositional logic.

PAC-theory of machine learning

**Session 3**

Distribution functions.

Multivariate random variables

**Session 4**

Multivariate random variables.

Marginal density, Independence,

Density of a transformed random vector,

Exponential families of distributions

THEORY OF PROBABILITYAND STATISTICS

THEORY OFPROBABILITYAND STATISTICS

BIBLIOGRAPHY

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