Department of Computer Science and Technology

Course pages 2017–18

Machine Learning and Algorithms for Data Mining

The assessment will consist of:

  • 10% - Two ticked practical exercises (5% each)
    Ticked practical 1: 2 November 2017 2-4pm, in class
    SVM practical instructions

    Ticked practical 2: 2 February 2018 9-11am, in class

  • 45% - Project 1 - Reconstruction of research paper results and a written report on the analysis of at most 2500 words;
    Deadline for Report 1: 29 November 2017, 16:00

  • 45% - Project 2 - Coding practical and written report on the practical of at most 2500 words.
    Deadline for Report 2: 23 February 2018, 16:00

Project/Assessment 1

Students will study a recent research paper that focuses on one of the topics of the course, redo the analysis with potential personal modifications or additional tests, and comment on these. The report should report on the methodology, analysis and results carried out by the student, with explanations of deviations to the original analysis in the paper. The report should be at most 2500 words.

Detailed instructions for Assessment 1

Project/Assessment 2

Students will carry out a project where they will be given a large data set (which may come from a range of different types of data sets) and will be asked to implement a particular machine learning algorithm (which will have been covered in the course), and then run an analysis on the provided data set using their implementation. The students will then write a 2500 word project report on their analysis of the data set resulting from applying their own implementation of the algorithm.

Online submission of assignments

The assignments may be submitted online using the course Moodle page.

You may, if you prefer, submit a hardcopy to the Graduate Education Office, FS03 instead.

Please note that the online submission link will deactivate at the deadline date. For late submissions, please hand in a hardcopy of the assignment to the Graduate Education Office.

Please complete and upload a coursework coversheet with each online submission.