General Information

Course Outline

The course is a basic introduction to machine learning, including:

  • supervised learning (mainly, classification)
  • un-supervised learning (such as clustering)
  • Bayesian methods

The course will include both theory and applied machine learning,
and a special emphasis will be put on machine learning algorithms.


Location and Hours

Please check the course schedule.


Homepage|nareh#nireplaH narE .forP
Homepage|ruosnam#ruosnaM yahsiY .forP
Homepage|flow#floW roiL .forP

Teaching Assistants:
Homepage moc.mehseg|onairam#niahcS onairaM
Feel free to coordinate reception hours with any of us via email.


  • Formal prerequisite: First year courses, and Tochna 1 and data structures.


Final Grade=0.60 exam+0.20 HW (1,2,3,4) + 0.20 HW (Final project)

As always, one has to pass the exam in order to pass the course.



  • T. Mitchel, Machine Learning

Reference books

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