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HU Credits:
2
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Dr. Or Zuk
Coordinator Office Hours:
Monday, 11-12
Teaching Staff:
Dr. Or Zuk
Course/Module description:
We will learn about classification and neural networks
Course/Module aims:
The goal is for students to learn the core principals of neural networks, to implement neural networks, apply them to image datasets and analyze classification performance.
Learning outcomes - On successful completion of this module, students should be able to:
To implement classifiers in R, to apply them to large scale datasets and to analyze results.
Attendance requirements(%):
none
Teaching arrangement and method of instruction:
I will explain the basic principals of neural networks. Some of learning of material will be by self reading.
Each group, consisting of one or two students, will implement a classifier and evaluate it on image data
Course/Module Content:
Neural Networks
Required Reading:
http://neuralnetworksanddeeplearning.com/
Additional Reading Material:
None
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 100 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
There will be a few computerize exercises during the semester and at the end where students will implement classifiers and apply them to image datasets - the grades will be determined by classification accuracy.
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