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Hubness-aware machine learning: an overview

This online lecture gives an introduction to hubness-aware machine learning. Hubness-aware classifiers, instance selection techniques and regression approaches are covered, while other hubness-aware machine learning techniques are mentioned too (e.g. hubness-aware clustering). The lecture is splitted into short videos, each one having a length of a few minutes. These videos are organized into a youtube playlist.

After watching the videos, you may want to check out the Self-Check Quiz.


Online course: Processing and Mining Biomedical Data

 

This online course aims to give an introduction into various data processing and analysis tasks related to biomedical data. In this course, we explain the basic techniques of data mining, such as classification, regression and matrix factorisation and show how these techniques can be used to biomedical problem. A separate module is devoted to the basic data processing tasks associated with next generation sequencing (NGS) data. Self-check quizes are attached to the course so that participants can check if they followed the concepts presented in the lecture.

No prior knowledge of biology or computer science is assumed from the students of the course, therefore we hope that the course may be useful both for a broad audience and university students as an introduction to the analysis of biomedical data.

The video lectures and self-check quizes of the course are available at
https://www.facebook.com/biomining14.
Enjoy!

Lecture slides (in PDF and ODP)

Lecture 1 - Revolution in Biology [PDF] [ODP]
Lecture 2 - What is Data Mining? [PDF] [ODP]
Lecture 3A, 3B - Matrix completion techniques (motivation), Matrix multiplication [PDF] [ODP]
Lecture 3C - Matrix completion (gradient descent) [PDF] [ODP]
Lecture 3D - Link prediction with matrix completion [PDF] [ODP]
Lecture 3E - The intuition behind Restricted Boltzmann Machines (RBMs) [PDF] [ODP]
Lecture 3F - RBMs for drug-target prediction [PDF] [ODP]
Lecture 4AB - Introduction to Classification, Some Basic Classifiers [PDF] [ODP]
Lecture 4CD - Preprocessing Time Series, Dynamic Time Warping [PDF] [ODP]
Lecture 4EF - Multivariate DTW, Speedup Techniques for Time Series Classification [PDF] [ODP]
Lecture 4GH - Instance selection for Time Series, Semi-supervised Classification [PDF] [ODP]
Lecture 5 - Basics of the Analysis of NGS Data [PDF] [ODP]

Acknowledgement -- While developing the is online course Processing and Mining Biomedical Data, Krisztian Buza was with the Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw, his position was funded by the post-doc fellowship of the Warsaw Center of Mathematics and Computer Science (WCMCS).