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.
Formalities
Location and Hours
Please check the course schedule.
Staff
- Instructors:
li.ca.uat|flow#floW roiL .forP li.ca.uat.tsop|nareh#nireplaH narE .forP
- Teaching Assistants:
moc.liamg|sveger#regiewhcS vegeRFeel 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 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.