General Information

Course Outline

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

  • Supervised learning (mainly, classification)
  • Unsupervised 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|flow#floW roiL .forP
Homepage|nareh#nireplaH narE .forP
Teaching Assistants:
Homepage moc.liamg|sveger#regiewhcS vegeR
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 is made out of:

  • 60% Exam
  • 20% Exercises
  • 20% Final Project

If exercise 0 was submitted, and it improves the grade, then the exercises' point allocation will be divided across the 5 exercises.

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

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