Machine Learning
- Provider edX
- Cost Free Online Course (Audit)
- Session Upcoming
- Language English
- Certificate $375 Certificate Available
- Effort 8-10 hours a week
- Duration 12 weeks long
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Overview
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.
Syllabus
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
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Reviews for edX's Machine Learning Based on 9 reviews
- 5 stars 56%
- 4 star 11%
- 3 star 0%
- 2 stars 22%
- 1 star 11%
Did you take this course? Share your experience with other students.
Write a review- 1
- 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 formative assessments (the only quizzes that we take are high pressure do or die quizzes)
- Many trick questions in the quizzes (for example: " Which of the following are NOT active learning strategies?")
- Ambiguous language ("This question tests a fundamental…
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.
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.
But:
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.
- 1