6.867是机器学习(Machine Learning)的导论课程。这门课提供了机器学习中许多技术及算法的概观，起始自如简单认知器算法(Simple Perceptrons)的课题，乃至于一些近来所关注的课题像是boosting， SVM， HMM， 以及Bayesian 网络。这门课赋予学生对现代机器学习技术基本的观念与直觉以及对于这些技术如何运作、为何有效等问题有比较正规化的认识。 隐含于这门课程之下的主题是统计推论(statistical inference)，这个隐含的主题成为机器学习中大多数方法的基础。

6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.