Machine Learning

Description

In this course, you will learn:

• For regression and classification, supervised learning techniques are used.
• For data modeling and analysis, unsupervised learning approaches are used.
• Perspectives that are probabilistic versus non-probabilistic.
• Model learning optimization and inference algorithms.

Syllabus:

• Maximum likelihood estimation, linear regression, least squares
• Ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
• Bayesian linear regression, sparsity, subset selection for linear regression
• Nearest-neighbor classification, Bayes classifiers, linear classifiers, perceptron
• Logistic regression, Laplace approximation, kernel methods, Gaussian processes
• Maximum margin, support vector machines, trees, random forests, boosting
• Clustering, k-means, EM algorithm, missing data
• Mixtures of Gaussians, matrix factorization
• Non-negative matrix factorization, latent factor models, PCA and variations
• Markov models, hidden Markov models
• Continuous state-space models, association analysis
• Model selection, next steps