Application, definitively the sweet spot of Machine Learning, it is where most of the money is made and is the thing that most people will relate to when they hear you do something AI related. Barely no one has heard of PageRank, yet everyone knows the Google Company, the same happens with pretty much every machine learning application out there, or any other theory. For example, Quantum Mechanics are the basis for most of the modern electronics we have.

So, if you don't really care on how the algorithms are working, and just care about applying the algorithms for the fame and glory, maybe developing an application might be right for you. Not to say you must entirely disregard the math, just that the math you'll have to go over won't be as dense as the ones statisticians have to use. While they go over 200 year old proofs, you'll go over 10 year old proofs, and while their proofs are 20 pages long, yours will be about 1 page (average).

I can't really emphasize how important is for you to learn the math behind the applications, even if you do not know exactly how is it working, at least is good that you have an idea of what the algorithm is doing. This way, if you have any errors, you can look for solutions in the right places instead of changing variables praying for something good to occur. Also you have bragging rights that you know more math than your peers at undergrad working as software engineers.

There are different machine learning applications, ranking, natural language processing, image processing, activity recognition, etc. However each of this problems have the difficulties and challenges. And different people may be suited for different applications.

How to find the application that best suits you? In my personal point of view, go over you passion. If you like dinosaurs, maybe you could apply recognition algorithms to detect structures and patterns in x-ray scans in the bones. If however, you like financial data, there are some work using Game Theory, and probability to increase your profit.

It would be crazy to try and list every laboratory that has an application and uses machine learning to solve it, instead, I'll list some of the most common machine learning algorithms, and how can you use them.

Of course this list is not exclusive, and there are thousands of different algorithms for the different applications, this is just to give you a head start of where to look and which algorithms you may find interesting to go over. I've chosen some of the most recent algorithms used to solve this problems, those published in ICML and NIPS from the last 10 years.

In future posts we will go over the algorithms and explain them. So if you have a particular interest, stay tuned, because this is just getting interesting.

If you wish to contact me, you can always do so at my Twitter account @leonpalafox, my Google+ account, or my personal webpage www.leonpalafox.com

Take care, and see you next time

It would be crazy to try and list every laboratory that has an application and uses machine learning to solve it, instead, I'll list some of the most common machine learning algorithms, and how can you use them.

Of course this list is not exclusive, and there are thousands of different algorithms for the different applications, this is just to give you a head start of where to look and which algorithms you may find interesting to go over. I've chosen some of the most recent algorithms used to solve this problems, those published in ICML and NIPS from the last 10 years.

- Financial Data: Markov Chains, Learning, Regression, Gaussian Processes, SVM
- Robotics: Kalman Filter, Markov Chain Monte Carlo Methods, Markov Decision Process (Reinforcement Learning), SVM
- Biology: Network Structure, Clustering, Network Parameters, Dirichlet Processes, Indian Buffet Processes, SVM
- Vision: Markov Random Fields, Belief Networks, Neural Networks, SVM
- Natural Language Processing: Conditional Random Fields, Latent Dirichlet Allocation, Mixture Models, SVM

In future posts we will go over the algorithms and explain them. So if you have a particular interest, stay tuned, because this is just getting interesting.

If you wish to contact me, you can always do so at my Twitter account @leonpalafox, my Google+ account, or my personal webpage www.leonpalafox.com

Take care, and see you next time

Can't wait for you to go over LDA. When you do go over it, please use examples, in addition to the math, so that it becomes easier to grasp.

ReplyDeleteAny pointers to related(/imp) Math topics are always welcome. :)

ReplyDeleteTo support your claims [1], could you please list some concrete examples?

ReplyDelete[1] "While they go over 200 year old proofs, you'll go over 10 year old proofs, and while their proofs are 20 pages long, yours will be about 1 page (average)."

Thanks for sharing your info. I really appreciate your efforts and I will be waiting for your further write ups thanks once again.

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