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6.867 is an introductory course on machine learning which gives an overview of many concepts, 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.
FREE
This course includes
Hours of videos
638 years, 9 months
Units & Quizzes
23
Unlimited Lifetime access
Access on mobile app
Certificate of Completion
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 Currilcum
- 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
- Probabilistic inference Unlimited
