Machine Learning (edX)

Start Date
This course is archived
Machine Learning (edX)
Course Auditing
Categories
Effort
Certification
Languages
Calculus. Linear algebra 
. Probability and statistical concepts.Coding and comfort with data manipulation.
Misc

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Machine Learning (edX)
Master the essentials of machine learning and algorithms to help improve learning from data without human intervention. 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.

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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.


What you'll learn:

- Supervised learning techniques for regression and classification

- Unsupervised learning techniques for data modeling and analysis

- Probabilistic versus non-probabilistic viewpoints

- Optimization and inference algorithms for model learning


Course Syllabus


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



MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
206.00 EUR
Calculus. Linear algebra 
. Probability and statistical concepts.Coding and comfort with data manipulation.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.