Project in Practical Machine Learning
Vuosi | Lukukausi | Päivämäärä | Periodi | Kieli | Vastuuhenkilö |
---|---|---|---|---|---|
2017 | kevät | 18.01-03.03. | 3-3 | Englanti | Johannes Verwijnen |
Luennot
Aika | Huone | Luennoija | Päivämäärä |
---|---|---|---|
Ke 16-18 | B119 | Johannes Verwijnen | 18.01.2017-25.01.2017 |
Pe 16-18 | B119 | Johannes Verwijnen | 03.03.2017-03.03.2017 |
Yleistä
The course moodle page is at https://moodle.helsinki.fi/course/view.php?id=23197
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 structure has been changed slightly from previous iterations:
Lecture date | Contents |
---|---|
Wed, Jan 18th, 16:15 | Admnistrative issues, topic introduction, scope, dirtiness and context, existing tools & libraries |
Wed, Jan 25th, 16:15 | Student topic presentations, discussion, data science in the industry |
Wed, Mar 1st, 16:15 | Student project presentations, discussion and take-aways. |
Kurssin suorittaminen
Lecture attendance is not mandatory, but very useful. Slides will be available on this page.
The project will be implemented either individually or in groups of 2-3 students. Each group will submit several milestone documents using the course moodle page for peer review. During the project guidance and simple clarifications are available via email. The final deliverable should be a written report about the chosen topic and results. A short presentation should be given on March 1st.
The number of study points awarded is dependent on the amount of work done on the project. Individual work hours need to be recorded during project work and shared using 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.
Kirjallisuus ja materiaali
You can find peer support (and the instructor) via Moodle. Please let the instructor know about any other sources you find interesting for inclusion.
Data Sources:
- http://en.wikipedia.org/wiki/List_of_financial_data_feeds
- https://ilmatieteenlaitos.fi/avoin-data
- http://www.infotripla.fi/digitraffic/doku.php?id=start_en
- Facebook: "Finnish Open Data Ecosystem" group
ML libraries (in no particular order):
- Java:
- Python:
Places to host your system:
- department's users-server http://www.cs.helsinki.fi/en/compfac/running-cgi-and-php-scripts-and-use-tomcat-containers
- https://www.heroku.com/
- https://cloud.google.com/appengine/