Complex Systems

Professor in charge:  Professor Jari Saramäki

Extent: 60 credits

Abbreviation: CS

Code: SCI3060

Objectives

The aim is to give the students a strong computational and theoretical background for understanding complex systems, from the human brain to a diversity of biological and social systems. The major has been structured such that the student can choose which areas to emphasize (e.g. neuroscience, theory of complex systems, machine learning). After completing their studies, the students have the necessary skills for interdisciplinary scientific careers, or, e.g. for data scientist positions in the industry.

Content and structure

The major has been structured to allow for flexibility, and the student may emphasize chosen areas of interest. In addition to courses common to all Life Science Technologies masters, the major has four modules: 1) Measuring and interpreting data, 2) Advanced statistics and machine learning, 3) Systems and Modeling, and 4) Application areas. The student must pick at least one course from each module; the student is free to choose the rest of the courses freely from all modules.

Descriptions of the modules:

1. Measuring and interpreting data: courses for dealing with experimental data, its analysis, and visualization.

2. Advanced statistics and machine learning: Bayesian statistics and methods, basic principles of machine learning.

3. Network and systems: network science, chaos theory, non-equilibrium statistical mechanics, mathematical modeling.

Application areas: courses in all other Life Science Technologies majors.

Code

Course

Credits

Period/Year

Compulsory common courses of the programme (15 credits):

MS-E2115

Experimental and Statistical Methods in Biological Sciences

5

I-II/1

JOIN-E3000

Life Science Technologies Project Course

10

III-V/1

Compulsory courses of the major (45 credits). Pick at least one course from each four modules:

1 Measuring and intepreting data

CS-E4840

Information Visualization

5

IV/1

CS-E5700

Hands-on Network Analysis

5

IV-V/1

MS-E2112

Multivariate statistical analysis

5

III-IV/1

2 Advanced statistics and machine learning

CS-E5710

Bayesian Data Analysis

5

I-II/1

CS-E5720

Work Course on Bayesian Analysis

2

III-V/1

CS-E3210

Machine Learning: Basic Principles

5

I/2

CS-E4600

Algorithmic Methods of Data Mining

5

I/1 or 2

CS-E4890

Deep Learning

5

II/2

CS-E4070

Special Course in Machine Learning and Data Science

1-10

year 2

3 Networks and systems

CS-E5740

Complex Networks

5

I-II/1

CS-E5745

Mathematical Methods for Network Science

5

III/1 or 2

CS-E5880

Modeling Biological Networks

5-7

III/1

CS-E5790

Computational Science 5  I-II/1

CS-E5755

Nonlinear Dynamics and Chaos 5 III-IV/I or 2

CS-E5780

Special Assignment in Complex Systems

5-10

year 2

CS-E5770

Special Course in Complex Systems

3-6

1 or 2

 

 

   

4 Application areas

Pick any course from the other Life Science Technologies majors.

 

Recommendations for elective studies

In their elective studies, the students are encouraged to take courses from other majors of the LifeTech programme, according to their interests. Courses in the field of information and computer science are also recommended. Also internship is recommended in elective studies.

 

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