|
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 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(%):
100
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:
None
Additional Reading Material:
None
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)
|
