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Weve detected that JavaScript is disabled in this browser. Please enable JavaScript or switch to a supported browser to continue using twitter.com. You can see a list of supported browsers in our Help Center. Help Center Future dates to be announcedFrequently Asked QuestionsInstructor-led on a course scheduleOptional upgrade availableFuture dates to be announcedMachine Learning is the basis for the most exciting careers in data analysis today. Youll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.Major perspectives covered include:probabilistic versus non-probabilistic modelingsupervised versus unsupervised learningTopics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.Institution: ColumbiaXSubject: Computer ScienceLevel: AdvancedPrerequisites: CalculusLinear algebra Probability and statistical conceptsCoding and comfort with data manipulationLanguage: EnglishVideo Transcript: EnglishSupervised learning techniques for regression and classificationUnsupervised learning techniques for data modeling and analysisProbabilistic versus non-probabilistic viewpointsOptimization and inference algorithms for model learningWeek 1: maximum likelihood estimation, linear regression, least squaresWeek 2: ridge regression, bias-variance, Bayes rule, maximum a posteriori inferenceWeek 3: Bayesian linear regression, sparsity, subset selection for linear regressionWeek 4: nearest neighbor classification, Bayes classifiers, linear classifiers, perceptronWeek 5: logistic regression, Laplace approximation, kernel methods, Gaussian processesWeek 6: maximum margin, support vector machines, trees, random forests, boostingWeek 7: clustering, k-means, EM algorithm, missing dataWeek 8: mixtures of Gaussians, matrix factorizationWeek 9: non-negative matrix factorization, latent factor models, PCA and variationsWeek 10: Markov models, hidden Markov modelsWeek 11: continuous state-space models, association analysisWeek 12: model selection, next stepsUnfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live. Weve detected that JavaScript is disabled in this browser. Please enable JavaScript or switch to a supported browser to continue using twitter.com. You can see a list of supported browsers in our Help Center. 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