Master's Programme in Data Science is responsible for the course.
The course is available to students from other degree programmes based on separate admission. Priority will be given to students of the
Master’s Programme in Data Science.
Academic Writing for Students in English-Medium Master's Degree Programmes 2 to be completed concurrently with the seminar.
Prerequisite: Introduction to Machine Learning.
Distributed Data Infrastructures.
The student can find information on a given topic in scientific literature and write a scientific report and deliver an oral presentation based on this material.
During or after the first year of MSc studies.
The seminar will cover machine learning methods that can learn from data distributed across a network. Distribution of the data poses several unique challenges such as memory and communication needs, scalability, efficiency. The data may be fragmented horizontally (different instances of data at different sites) or vertically (subsets of attributes stored at different sites). Several aspects of the methods will be covered, such as software architectures, optimisation problems, algorithms, deep learning problems.
The students can select their topic among alternatives provided from the following themes :
Software architectures and frameworks for distributed machine learning
Distributed learning with privacy constraints (e.g. cryptographic techniques, differential privacy)
Distributed deep learning (such as DistBelief)
Parallel Bayesian inference (e.g. parallel MCMC methods)
Vertically distributed data
Some topics will be more theoretical and others more computational.
Mostly relevant topical scientific articles, list of articles provided by the instructor.
The students will agree upon a specific topic with the instructor and write a scientific report and deliver a scientific oral presentation on the topic.
The students are expected to participate actively in the seminar sessions. Seminar includes also peer review of other students' reports and presentations.
Grading scale 1-5. The grading will combine evaluation of the written report, the oral presentation and other course participation.
Compulsory attendance at seminar sessions.
Written report, oral presentation, peer feedback on the presentations and reports of other students.
Post doc Antti Koskela
Assoc. Prof. Antti Honkela