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Columbia University

Machine Learning

Columbia University via edX

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Machine Learning is the basis for the most exciting careers in data analysis today. You’ll 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 modeling
  • supervised versus unsupervised learning

Topics 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.


Week 1: maximum likelihood estimation, linear regression, least squares
Week 2: ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
Week 3: Bayesian linear regression, sparsity, subset selection for linear regression
Week 4: nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron
Week 5: logistic regression, Laplace approximation, kernel methods, Gaussian processes
Week 6: maximum margin, support vector machines, trees, random forests, boosting
Week 7: clustering, k-means, EM algorithm, missing data
Week 8: mixtures of Gaussians, matrix factorization
Week 9: non-negative matrix factorization, latent factor models, PCA and variations
Week 10: Markov models, hidden Markov models
Week 11: continuous state-space models, association analysis
Week 12: model selection, next steps

Taught by

Professor John W. Paisley


4.0 rating, based on 11 Class Central reviews

Start your review of Machine Learning

  • Anonymous
    Following are the problems with the course: - No diagrams - No examples - No clarity if a particular variable is a scalar, vector or matrix - Professor simply reads from the slides and does not add much to what forms part of the slide deck. - No f…
  • There are not many courses online that provide such in-depth learning experience in Machine Learning. This course goes into some details and mathematics of the algorithms being used. It demands a good amount of time every week to understand and apply all that is being taught but that is what makes it good. It is not like many other courses that you can take and pass with minimal effort but at the end of it, it is worth spending time taking this course.
  • Beautiful course which covers advanced concepts of machine learning. Professor Paisley covers a whole range of topics and breaks down hard concepts clearly. This course is very theoretical. There are four programming assignments which give an opportunity implement some of the algorithms learned. The only downside is that weekly assignments lack mathematical/programming rigor which can be improved in future sessions. Overall must do course for anyone interested in this topic.
  • Anonymous
    A great introduction to machine learning for those who are well grounded in the mathematics of undergraduate level. It explains the algorithm and the mathematical background of various machine learning techniques very clearly.

    Only one point I did not like about this course was that the assignments were not well organized. I hope it will be improved in the future.
  • Sensei
    This course requires a solid foundation on probabilities, calculus, linear algebra and programming. Provided these prerequisites are available (anyone who is serious about the field should possess these skills anyway), the course will become an incredibly useful resource to break into Machine Learning. The only downside I found is that neural networks is not covered. A great deal of the current breakthroughs in ML are happening in this area!
  • Profile image for David Chen
    David Chen
    I signed up for this course as a supplement while taking a theoretical Machine Learning course in graduate school. This MOOC is very theoretical -- the start of the course with Multivariate Gaussian was pretty intense in my opinion, but, quite unique comapred to other machine learning / data science courses out there. I thought the course laid a great foundation for me to successfully complete my in-person class. The lecture sldies in this course were very organized, clearly presented, and covered a great amount of useful material. The instructor Dr. John P was very knowledgeable and engaging. The quizzes and Octave programming homework were very challenging, but that's what will make you learn, right?
  • Anonymous
    Some weeks are better than others. In general, too much emphasis on algebra, and not enough on geometric interpretation of algebra and solutions. I found myself cruising the web for better explanations.
    In this respect, the class I am listening to right now, the class on support vector methods is pretty bad.

    I am not taking class for certification, but I have looked at the quizzes and I agree they are not good.
    Questions are about little finicky points of the subject matter, and not much else. The course is pretty worthless without some coding with explanations, and there is not much of that here.
    I am going to go to Ng next even if he does not use python to implement.
  • Sedat
    Amazing in-depth knowledge. Almost all algorithms out there are being explained.

    Terrible execution, lecturer is just reading the slides and explaining a bit. Not enough real life examples or any code examples. No interactivity. Reading a book on ML would help more.

    No good examples. No practices. Assignments are a bit off.
  • Anonymous
    The content of course is very good. Mathematical concepts covered are up to mark. Quiz and Assignments gives good insight about ML.
  • Anonymous
    I have completed the course since I was supposed to teach an easier undergraduate course in our college. It is a wonderful theoretical course based on the classic books.

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