Credits: 5

Schedule: 07.01.2019 - 15.02.2019

Contact information for the course (applies in this implementation): 

Professors Visa Koivunen, Sergiy Vorobyov, Esa Ollila (e-mail:

Please contact by email. 

Teaching Period (valid 01.08.2018-31.07.2020): 

III - IV 2018 – 2019, 2019 – 2020 (spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

Students are able to recognize the problems of processing data of large scale problems that arise in engineering and computer science. Students can describe the basic theory of such problems, concentrating on results that are useful in computation. Students have a thorough understanding of how such problems are thought of and addressed, and some experience in solving them. Students can apply the methods in their own research work. Students know a number of examples of successful application of the techniques for signal processing of large scale data. More detailed and revised learning outcomes are presented at the beginning of the course.

Content (valid 01.08.2018-31.07.2020): 

Optimization, subgradient methods, statistical learning.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Lectures, exercises, assignments.

Workload (valid 01.08.2018-31.07.2020): 

Lectures, excercises approximately 50 h, assignments and independent studying approximately 83 h, total 133 h

Attendance in some contact teaching may be compulsory.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

ELEC-E5430 Signal Processing for Large Scale Data Analysis L

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

Recommended ELEC-E5422 Convex Optimization I P and ELEC-E5440 Statistical Signal Processing P

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

In WebOodi

Further Information (valid 01.08.2018-31.07.2020): 

Language Class 3: English


Registration and further information