General Information
Table of Contents
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.
Formalities
Location and Hours
Please check the course schedule.
Staff
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- Teaching Assistants:
Feel free to coordinate reception hours with any of us via email.
Prerequisites
- Formal prerequisite: First year courses, and Tochna 1 and data structures.
Grade
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.
Reading
Textbook
- T. Mitchel, Machine Learning
Reference books
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