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I'm not sure what you are imagining the scope of your first project to be, but I would recommend you begin by understanding and implementing some well-known alg

I'm not sure what you are imagining the scope of your first project to be, but I would recommend you begin by understanding and implementing some well-known algorithms. Start with a Guassian Mixture model trained with the EM algorithm. Then do linear and logistic regression, perceptron is pretty simple and a simpler version of the widely used support vector machine. The handwriting recognition database here is fantastic for testing a variety of simple ML models: http://yann.lecun.com/exdb/mnist/ http://yann.lecun.com/exdb/mnist/ In our machine learning class, we would use data from the KDD cup for our projects. Why don't you create a submission for old KDD cups and see if your model can do better than random? 1998 is good for logistic and linear regression: http://www.kdnuggets.com/meetings/kdd98/kdd-cup-98.html http://www.kdnuggets.com/meetings/kdd98/kdd-cup-98.html Using the 2007 dataset, you can try out some of the matrix factoring methods that have worked well for the Netflix prize: http://www.cs.uic.edu/~liub/Netflix-KDD-Cup-2007.html http://www.cs.uic.edu/~liub/Netflix-KDD-Cup-2007.html These should all be relatively small tasks. Learning how to interpret your results and iterate your models to make them better will take longer than understanding the algorithms.

danger 16y ago
There are so many possible directions that somebody might want to go, though. If you're ultimately interested in reinforcement learning, you don't immediately need to understand EM in order to "get" Q learning. Or to work on discrete problems related to computer vision, having a basis in linear programming and algorithms will get you further than logistic regression or SVMs. I'll agree that anybody doing machine learning should understand the basic idea of maximum likelihood learning (e.g., logistic regression). But how far do you go beyond that? This is really the heart of the issue, I think. Yes, there are a ton of things that are really useful to know, and any machine learning student should learn them at some point. If you're trying to do an interesting project that teaches you something about research (and thus looks good on a graduate school application), though, what is the best use of your time?
- dododo 16y ago
  You're right in that you need direction. Each subclass of machine learning has some elementary algorithms worth learning: For unsupervised learning or probabilistic latent models, the EM algorithm, Metropolis-Hastings sampling, variational methods (variation EM, variational Bayes, expectation propagation). For supervised learning, linear regression, perceptrons, support vector machines. For reinforcement learning, Q-learning, E^3, Rmax.

danger 16y ago

There are so many possible directions that somebody might want to go, though. If you're ultimately interested in reinforcement learning, you don't immediately need to understand EM in order to "get" Q learning. Or to work on discrete problems related to computer vision, having a basis in linear programming and algorithms will get you further than logistic regression or SVMs. I'll agree that anybody doing machine learning should understand the basic idea of maximum likelihood learning (e.g., logistic regression). But how far do you go beyond that? This is really the heart of the issue, I think. Yes, there are a ton of things that are really useful to know, and any machine learning student should learn them at some point. If you're trying to do an interesting project that teaches you something about research (and thus looks good on a graduate school application), though, what is the best use of your time?

dododo 16y ago
You're right in that you need direction. Each subclass of machine learning has some elementary algorithms worth learning: For unsupervised learning or probabilistic latent models, the EM algorithm, Metropolis-Hastings sampling, variational methods (variation EM, variational Bayes, expectation propagation). For supervised learning, linear regression, perceptrons, support vector machines. For reinforcement learning, Q-learning, E^3, Rmax.