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Syllabus Advanced data analysis II: data vs. theory - 77743
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Last update 19-11-2018
HU Credits: 3

Degree/Cycle: 2nd degree (Master)

Responsible Department: Physics

Semester: 2nd Semester

Teaching Languages: Hebrew

Campus: E. Safra

Course/Module Coordinator: Prof Yehuda Hoffman

Coordinator Email:

Coordinator Office Hours: upon appointment

Teaching Staff:
Prof Yehuda Hoffman

Course/Module description:
The course develops advanced methods of analyzing and simulating experimental/observational data. The main focus is on the confrontation of theory with data: parameters estimation, hypothesis testing and Bayesian inference. Inference without theory by machine learning will be introduced

Course/Module aims:
To introduce students to up to date ideas and concepts and expose and train them with state of the art tools of (big) data analysis.

Learning outcomes - On successful completion of this module, students should be able to:
Conceptual and practical know-how of big data analysis

Attendance requirements(%):

Teaching arrangement and method of instruction: lectures

Course/Module Content:
1. Review: statistics and probability
2. Frequentist vs. Bayesian analysis
3. Fourier analysis: FFT, power spectrum and correlation functions
4. Signal and image processing: filtering and noise reduction
5. Random variables and random fields: random and constrained realizations
6. Theory vs. data: parameters estimation, Fisher matrix, hypothesis testing
7. Bayesian inference in (numerical) practice: Monte Carlo Markov Chains (MCMC)
8. Inference without theory: artificial neural networks (also known as machine learning)

Required Reading:

Additional Reading Material:

Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 75 %
Assignments 25 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %

Additional information:
prerequisite: advanced data analysis (77742)
Students needing academic accommodations based on a disability should contact the Center for Diagnosis and Support of Students with Learning Disabilities, or the Office for Students with Disabilities, as early as possible, to discuss and coordinate accommodations, based on relevant documentation.
For further information, please visit the site of the Dean of Students Office.