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HU Credits:
3
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Programming Instruction Unit
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
E. Safra
Course/Module Coordinator:
royi zidon
Coordinator Office Hours:
Teaching Staff:
Dr. royi zidon
Course/Module description:
The course will cover theoretical basics with practical applications of broad range of machine
learning concepts and methods
Course/Module aims:
Developing practical machine learning and data
science skills with python.
Learning outcomes - On successful completion of this module, students should be able to:
● Understanding the principal models used in machine learning
● Understanding the strengths
and weakness of each model.
● Determine which model or models would be most appropriate in different problems.
● Apply the principal models in python
Attendance requirements(%):
Teaching arrangement and method of instruction:
Course/Module Content:
Python, visualization, feature extraction,supervised and unsupervised learning, classification, clustering, regressions,
genetic algorithm, introduction to deep learning
Required Reading:
None
Additional Reading Material:
Introduction to Machine Learning, Third Edition , Ethem Alpaydin, MIT
Press, 2014
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition , Aurélien Géron
Grading Scheme :
Written Exam % 90
Submission assignments during the semester: Exercises / Essays / Audits / Reports / Forum / Simulation / others 10 %
Additional information:
Open to undergraduate students that passed a course in python
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