About this courseSkip About this course
Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.
Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.
What you'll learnSkip What you'll learn
○ Techniques for supervised learning including classification and regression.
○ Algorithms for unsupervised learning including feature extraction.
○ Statistical methods for interpreting models generated by learning algorithms.
Mistake Bounded Learning (1 week)
Decision Trees; PAC Learning (1 week)
Cross Validation; VC Dimension; Perceptron (1 week)
Linear Regression; Gradient Descent (1 week)
Boosting (.5 week)
PCA; SVD (1.5 weeks)
Maximum likelihood estimation (1 week)
Bayesian inference (1 week)
K-means and EM (1-1.5 week)
Multivariate models and graphical models (1-1.5 week)
Neural networks; generative adversarial networks (GAN) (1-1.5 weeks)
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Who can take this course?
Unfortunately, learners from 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.