Top /r/MachineLearning posts, Jan 11-17

SVMs, open source datasets, Bayesian decision theory, game AI, and deep learning visualizations are all featured in the past week's top /r/MachineLearning posts.

By Grant Marshall.

This week on /r/MachineLearning, we there have been some interesting posts and videos, in particular.

1. Support Vector Machines - A wonderful lecture by MIT professor Patrick Winston - [49:34] +90

This video lecture, part of MIT 6.034 Artificial Intelligence, offers a clear introduction to SVMs including the mathematics behind them without being too arcane for an advanced undergraduate viewer. In the comments, /u/Eagle-Eye-Smith notes “The entire series is a great source of information for anyone wanting to learn about artificial intelligence, machine learning and pattern recognition,” so be sure to check out other videos from the course that pique your interest.

2. A growing list of free and open-source datasets +82

This post is interesting not just because it has some good datasets (there are many place to find great datasets) but because it focuses on open-source datasets. Most of these datasets should offer some form of open-source license, though I would check the particular dataset you’re interested to be sure.

3. An easygoing introduction to Bayesian decision theory +72

This blog post is a nice introduction to Bayesian decision theory at a relaxed pace, which works great for developing intuition if. If these sorts of tutorials are your cup of tea, definitely check it out.

4. Winning Angry Birds AI - [14:01] +64

This YouTube video shows the winning submission to the AIBirds challenge at work. If you’re interested in AI applications or games, this is definitely worth looking at, and if this seems like the sort of thing you’re interested in, the 2015 competition still has an open call for participation.

5. Visualizing Representations: Deep Learning and Human Beings +63

small and large cluster visualization

This post has some very beautiful visualizations of different NN and deep learning based algorithms. My favorite is the interactive visualization of Wikipedia articles by category. One warning, though: the webpage is quite heavyweight and may take some time to load.