I am looking for some entry level posts about machine learning. Can anyone suggest anything for somebody new to this subject?
I am looking for some entry level posts about machine learning. Can anyone suggest
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I am looking for some entry level posts about machine learning. Can anyone suggest anything for somebody new to this subject?
By ‘posts’ i’ll assume you mean any resource available online.
I recommend two groups of resources:
First, find Machine Learning blogs in which the blogger’s preferred language is the same as yours. In my experience, reading a blog post on a single subject (e.g., SVM) while reading through the author’s source code supplied along with blog post is about the best way for a programmer to learn ML. A couple of excellent examples are the blogs Smell the Data (Python), and Igvita (Ruby). Both contain (at least) several posts each describing, tutorial-style, specific ML techniques, which include close walk-throughs of their (posted) source code. Igvita, in particular, has excellent tutorials with working Ruby code on Support Vector Machines, Decision Trees, Singular Value Decomp, and Ensemble Methods–like, the other blog i mentioned, an upper-level undergraduate course could be taught based solely on the ML posts in either blog.
Second, I highly recommend VideoLectures.net.
This is by far the best source–whether free or paid–i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) video lectures and tutorials on machine learning. The target audience for these video lectures ranges from beginner (some lectures are specifically tagged as “tutorials”) to expert; most of them seem to be somewhere in the middle.
All of the lectures and tutorials are taught to highly experienced professionals and academics, and in many instances, the lecturer is the leading authority on the topic he/she is lecturing on. The site is also 100% free.
The one disadvantage is that you cannot download the lectures and store them in e.g., itunes; however, nearly every lectures has a set of slides which you can download (or, conveniently, you can view them online as you watch the presentation).
A few that i’ve watched and that i can recommend highly:
Semi-Supervised Learning Approaches
Introduction to Machine Learning
Gaussian Process Basics
Graphical Models
k-Nearest Neighbor Models
Introduction to Kernel Methods