Project in Practical Machine Learning

582739
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
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:

ML libraries (in no particular order):

Places to host your system: