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Advanced ML (Spring Semester 2017) Mini-Project Website

For the literature survey students should sign up as a team of two. For the coding project, the teams can be composed of two, up to three students . Coding projects can be done individually, as well. A maximum of two teams can share the same coding project. Please, sign-up for lit. survey/mini-project in the following doodle by March 10 2017: ML Mini-Project Doodle

Reports (+ code) for the lit. survey and mini-project must be electronically submitted on the course moodle webpage by May 19 2017: Moodle Submission Link

Oral presentations will take place on May 26 2017: Schedule will be defined later on.

Schedule of the project presentations Schedule

Details and Related Documents for Lit. Survey and Mini-Projects

A full description of mini-project in pdf format can be found here: mini-project description PDF

Latex template for both literature survey and coding report:

Teams for mini-project Teams

Mini-Project (Coding)
Literature Survey
Isomap and Laplacian Eigenmaps

Locally Linear Embedding (LLE), Modified locally linear embedding (MLLE) and Hessian locally linear embedding(HLLE)

Stochastic neighbour embedding (SNE) and t-distributed stochastic neighbour embedding (t-SNE)

Support Vector vs. Relevance Vector Machine for regression (SVR vs. RVR)

Parametric (GMM) vs Non-Parametric Mixture Model (DP-GMM) Learning for Regression

Writing a literature survey is not trivial, you can find some advice for the redaction here : A Guide to Writing the Disseration Literature Review.

Here are some usefull links to get started in the literature survey:

The available subjects are the following:

Methods for kernel learning

Methods for active learning

Data mining methods for crawling mailboxes

Data mining methods for crawling git-hub

Classification methods for spam/no-spam

Pros and cons of crowdsourcing

Recent trends and open problems in speech recognition

Ethical issues on data mining