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:

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