+ ## Section XV (Anomaly Detection)
+ * [gaussian distribution](gaussian_distribution)
+ * [multivariate gaussian distribution](multivariate_gaussian_distribution)
+ ## Section XVI (Recommender Systems)
+ * [matrix factorization](matrix_factorization)
+ * optional: also take a look at [factor analysis](factor_analysis) for a probabilistic perspective on matrix factorization
+ ## Section XVII (Large Scale Machine Learning)
+ * [stochastic gradient descent](stochastic_gradient_descent)
+ ## Section XVIII (Application Example: Photo OCR)
+ * sorry, nothing for this section
+ ## What next?
+ * Consider taking one of the following Coursera courses to further your machine learning knowledge:
+ * Daphne Koller's [Probabilistic Graphical Models Course](https://www.coursera.org/course/pgm) -- it's quite a bit harder than Andrew's machine learning course, but if you're serious about doing machine learning (e.g. research or professionally) then you will need to learn this content at some point (you'll save yourself a lot of pain if you start on it early).
+ * Geoffry Hinton's [Neural Networks for Machine Learning Coursera course](https://www.coursera.org/course/neuralnets)
+ * Pedro Domingos's [Machine Learning Coursera course](https://www.coursera.org/course/machlearning) (this is more oriented toward applied machine learning & data mining).
+ * EdX offer's a [fantastic artificial intelligence course](https://www.edx.org/course/uc-berkeley/cs188-1x/artificial-intelligence/579) that will give you a broader view of AI.
+ * Udacity has a [course on robotics](https://www.udacity.com/course/cs373) that will show you how to use some of your new machine learning knowledge to program an autonomous vehicle.
+ * Consider working through the material in Roger Grosse's excellent [Bayesian Machine Learning Roadmap](http://metacademy.org/roadmaps/rgrosse/bayesian_machine_learning). Do you understand [Markov Chain Monte Carlo inference](markov_chain_monte_carlo), [mixture of Gaussians model](mixture_of_gaussians), [hidden Markov models](hidden_markov_models), or the [junction tree algorithm](junction_trees)? These concepts, and many more in his roadmap, should be in the toolbelt of any machine learner worth his/her salt.|coursera_ml_supplement|1|6|Coursera Machine Learning Supplement|Colorado Reed|PUB_MAIN|Coursera Machine Learning Students|1|This is a supplement to Andrew Ng's Coursera machine learning course.