LEARNING OUTCOMES
Students will be able to recognize the problems of processing data of large scale that arise in engineering and computer science. The focus is in statistical inference, learning and optimization methods for large scale data. Students will know the basic theory of such problems, concentrating on results that are useful in computation. Students will have a thorough understanding of how such problems are thought of and addressed, and some experience in solving them. Students are expected to be able to apply the methods of large scale data analysis in their own research work. They will know a number of examples of successful application of the large scale data analysis techniques in different areas. More detailed and revised learning outcomes are presented at the beginning of the course.
Credits: 5
Schedule: 10.01.2023 - 15.02.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Sergiy Vorobyov, Visa Koivunen, Esa Ollila
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:
Large and huge scale optimization, statistical learning methods of large scale data analysis, multiple hypothesis testing and false discovery rate control for large scale and distributed data, exploiting sparsity in large scale data analysis, tensor-based methods for analysis data with multiple atributes. Sequential decision making and change-point detection.
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:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III
2023-2024 Spring III