The aim of this course is to discuss modern techniques for analyzing, interpreting, visualizing and exploiting the data that is captured in scientific and commercial environments. The course will develop the ideas taught in other machine learning modules (IAML, MLPR, PMR) and discuss the issues in applying them to real-world datasets, as well as teaching about other techniques and data-visualization methods.
This year, the course comprises two parts:
The first part is a series of lectures on various aspects Data Mining.
The second part will consist of student presentations of papers relating to relevant topics. Students will also carry out a practical mini-project on a real-world dataset. For both paper presentations and mini-projects, students have to work in groups. Lists of suggestions are available, but students may also propose their own, subject to approval from the instructor.
This is a course for MSc level students that runs in semester 2. PMR and MLPR are co-requisites for this course. The course descriptor can be found here.
There is one 2-hour DME lecture per week.
Details regarding the lectures can be accessed via the link at the top of the page.
Lab sessions start in week and run through to week 6.
Details regarding the labs can be accessed via the link at the top of the page.
Students are expected to present a case-study based on a data mining paper of their choice. Paper presentations should be done in groups of 3 students.
Additionally, students are expected to carry out a practical mini-project on a real-world dataset. For this, students may work in groups of 3 or 4.
Details regarding the paper presentations and mini-projects can be accessed via the link at the top of the page.
A web discussion site is available via nb.mit.edu. You should receive an email about this.
Please give us your feedback on the course - it helps a lot in improving it for next year's students. The link for feedback is here (this link is live from late March to late May) and you will be able to see the results of the surveys here.
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