MIT OCW Machine Learning Courses Information


In this post, you get the information related to MIT OCW machine learning course from MIT OpencourseWare (OCW). They use  Matlab as the primary programming environment. The documentation for Matlab could be found on this page, Matlab Documentation.  The course is provided by Electrical Engineering and Computer Science department.

Other related courses which could be useful for data scientist / machine learning engineers are some of the following:

Lecture Notes – Machine Learning Course

Lecture notes could be found on the following topics:

  • Introduction, linear classification, perceptron update rule (PDF)
  • Perceptron convergence, generalization (PDF)
  • Maximum margin classification (PDF)
  • Classification errors, regularization, logistic regression (PDF)
  • Linear regression, estimator bias and variance, active learning (PDF)
  • Active learning (cont.), non-linear predictions, kernals (PDF)
  • Kernal regression, kernels (PDF)
  • Support vector machine (SVM) and kernels, kernel optimization (PDF)
  • Model selection (PDF)
  • Model selection criteria (PDF)
  • Description length, feature selection (PDF)
  • Combining classifiers, boosting (PDF)
  • Boosting, margin, and complexity (PDF)
  • Margin and generalization, mixture models (PDF)
  • Mixtures and the expectation maximization (EM) algorithm (PDF)
  • EM, regularization, clustering (PDF)
  • Clustering (PDF)
  • Spectral clustering, Markov models (PDF)
  • Hidden Markov models (HMMs) (PDF)
  • HMMs (cont.) (PDF)
  • Bayesian networks (PDF)
  • Learning Bayesian networks (PDF)
  • Guest lecture on collaborative filtering (PDF)

The instructors for the course (currently) are the following:

Download Machine Learning Course Materials

The entire course material can be downloaded from this page (Download Course Materials). This is the direct download link.


In this post, you got the information about three different MIT OCW machine learning courses which could be useful for machine learning engineers/ data scientists. These courses are machine learning, introduction to probability, introduction to computational thinking and data science. All of the course materials (video lectures and lecture notes) are free for download and you could get started with self-paced learning anytime, anywhere.



Ajitesh Kumar

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