Reddit reviews Machine Learning for Hackers

I liked Machine Learning For Hackers, Programming Collective Intelligence and The Elements of Statistical Learning.

Off-course R is used for machine learning. It's probably the most popular language for interactive exploratory and predictive analytics right. For instance most winners of kaggle.com machine learning competitions use R at one point or another (e.g. packages such as randomForest, gbm, glmnet and off-course ggplot2). There is also a recent book specifically teaching how to use R for Machine Learning: Machine Learning for Hackers.

My-self I am more a Python fan so I would recommend python + numpy + scipy + scikit-learn + pandas (for data massaging and plotting).

Java is not bad either (e.g. using mahout or weka or more specialized libraries like libsvm / liblinear for SVMs and OpenNLP / Standford NLP for NLP).

I find working in C directly a bit tedious (esp. for data preparation and interactive analysis) hence better use it in combination with a scripting language that has good support for writing C bindings.

I'd say that the answer to your question depends on the problem. For certain problems, such as detecting faces, there are functions out there that do everything for you. For other problems, such as circle detection, combinations of existing functions will get the job done (as MakingMacaroni describes in another comment). Then for some problems, such as abandoned luggage detection in airports, you really do need to be up on the current research and have a solid grasp of the mathematics involved.

I'd say that the task you're describing is probably in the second or third category. You might try thresholding optical flow over time, as RGKaizen suggests. Depending on how much training data you have to work with, you might also try training a machine learning classifier on one or more visual features to generate profiles of "normal" and "emergency" situations. If you expect big green tanks to appear or fires to break out, blob detection with color histogram analysis might even do the trick. They key is to make the problem as easy for the computer as possible, and figure out which of the functions OpenCV gives you are best suited for your particular situation.

Best of luck! If you go forward on this project, please let us know what you come up with!

EDIT: Here are a few resources for figuring out which functions to use, what math to apply, etc.:

• http://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714
  
• http://www.amazon.com/Machine-Vision-Practicalities-Applications-ebook/dp/B008B5Q79O/ref=tmm_kin_title_0
  
• The O'Reilly Learning OpenCV book is a great combination of theory and application, but make sure you get the 2nd Edition, as the first edition refers to a deprecated version of the API. I recommend purchasing the digital edition from the O'Reilly website once it becomes available.

If you're interested in learning R for this stuff, check out the books Machine Learning for Hackers and Data Mining with R. If you have the time to take a survey course, check out the machine learning class on coursera (the assignments use octave, which is basically a matlab clone).

There have already been a few books listed focusing on theory, so I'll add Machine Learning for Hackers to the list.

It doesn't cover much of the theory, but it's a nice start to getting the programming skills you need for machine learning. When you start using these techniques on real data, you'll quickly see that it's almost never a simple task to go from messy data to results. You need to learn how to program to clean your data and get it into a usable form to do machine learning. A lot of people use Matlab, but since they're free I do all of my programming in R and Python. There are a lot of good libraries/packages for these languages that will enable you to do a lot of cool stuff.