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6.867 is an introductory course on machine learning which gives an overview of many concepts,
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English
English [CC]
FREE
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Description
techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
Course content
- Introduction, linear classification, perceptron update rule Unlimited
- Perceptron convergence, generalization Unlimited
- Maximum margin classification Unlimited
- Classification errors, regularization, logistic regression Unlimited
- Linear regression, estimator bias and variance, active learning Unlimited
- Active learning (cont.), non-linear predictions, kernals Unlimited
- Kernal regression, kernels Unlimited
- Support vector machine (SVM) and kernels, kernel optimization Unlimited
- Model selection Unlimited
- Model selection criteria Unlimited
- Description length, feature selection Unlimited
- Combining classifiers, boosting Unlimited
- Boosting, margin, and complexity Unlimited
- Margin and generalization, mixture models Unlimited
- Mixtures and the expectation maximization (EM) algorithm Unlimited
- EM, regularization, clustering Unlimited
- Clustering Unlimited
- Spectral clustering, Markov models Unlimited
- Hidden Markov models (HMMs) Unlimited
- HMMs (cont.) Unlimited
- Bayesian networks Unlimited
- Learning Bayesian networks Unlimited
- Guest lecture on collaborative filtering Unlimited
Instructor
Massachusetts Institute of Technology
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