LEARNING OUTCOMES
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.
Credits: 5
Schedule: 11.01.2022 - 16.02.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Visa Koivunen, Esa Ollila, Sergiy Vorobyov
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Optimization, subgradient methods, statistical learning.
Assessment Methods and Criteria
valid for whole curriculum period:
Lectures, exercises, assignments.
Workload
valid for whole curriculum period:
Lectures, excercises approximately 50 h, assignments and independent studying approximately 83 h, total 133 h
Attendance in some contact teaching may be compulsory.
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Period:
2020-2021 Spring III-IV
2021-2022 Spring III-IV
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=ELEC-E5431
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.
In WebOodi