Project in Practical Machine Learning
2-6
Algoritmit ja koneoppiminen
Syventävät opinnot
A project in implementing an online machine learning system. Each student (or pair) will create a ML system deployed on a webserver, periodically importing data over the internet and publishing its results. The system needs to be implemented using a webserver-friendly programming language and framework (ie. no R/MATLAB/Octave). The amount of credit points varies per group depending on group size and amount of work. Grading is based on a project report and possible presentation. Prerequisites: Introduction to Machine Learning and Scientific Writing (or similar knowledge). Students should be very fluent in the programming language/framework of their choice.
| Vuosi | Lukukausi | Päivämäärä | Periodi | Kieli | Vastuuhenkilö |
|---|---|---|---|---|---|
| 2016 | kesä | 18.05-17.08. | 5-6 | Englanti | Johannes Verwijnen |
Luennot
| Aika | Huone | Luennoija | Päivämäärä |
|---|---|---|---|
| Ke 14-16 | B119 | Johannes Verwijnen | 03.08.2016-03.08.2016 |
Yleistä
The purpose of the course is to introduce students to the problematics of machine learning in a realistic setting. Students should be able to identify and take into account the "dirtiness" of real online data; select, justify and implement a machine learning algorithm/technique using a programming environment runnable on a web server; monitor and report the accuracy of their implementation, including reflection of their choices.
The course starts on May 18th and the final deadline for the report is on August 17th. Students will be required to present their projects on August 3rd, which is the only formal meeting of this course.
Kurssin suorittaminen
The project will be implemented either individually or in pairs. Everyone will have a meeting with the instructor in the beginning of their project to validate the data source and implementation planned and to explain expected outcomes in detail. Another meeting will be scheduled roughly halfway through the project to ensure that the group is on schedule and refresh expectations. During the project guidance and simple clarifications are available via email.
The number of study points awarded is dependent on the amount of work done on the project. Higher amounts of study points require the implementation of a machine learning algorithm in the language of choice, whereas lower amounts can be achieved by using available libraries. Individual work hours need to be recorded during project work and submitted every Sunday (alternatively you can just share an online spreadsheet with the instructor). All project work should be available in a public GitHub repository.
The course is graded based on the written report and presentation.