# Course Identification

Statistics, Algorithms and Experiment design
20231062

## Lecturers and Teaching Assistants

Dr. Barak Zackay
Dotan Gazith, Jonathan Mushkin, Oryna Ivashtenko

## Course Schedule and Location

2023
Second Semester
Monday, 14:15 - 16:00, Drori Auditorium
Thursday, 14:15 - 16:00, Drori Auditorium
17/04/2023
21/07/2023

## Field of Study, Course Type and Credit Points

Physical Sciences: Lecture; Elective; Regular; 4.00 points
Chemical Sciences: Lecture; Elective; Regular; 4.00 points
Life Sciences: Lecture; Elective; Regular; 4.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 4.00 points

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## Prerequisites

Undergrad level knowledge of linear algebra and probability theory and computer programming.

60

English

## Attendance and participation

Expected and Recommended

Pass / Fail

50%
50%

Final assignment

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4

## Syllabus

• Hypothesis testing (Neyman - Pearson, UMP, sufficient statistics, discrete hypotheses, complex hypotheses)
• Matched filter, optimal weighting, least squares
• Fourier Analysis (convolution theorem, power spectrum, autocorrelation, FFT, Linear systems, basic signal processing, noise whitening)
• Dynamic programming (concept + 2/3 algorithms (Viterbi, Radon Transform, Kalman Filtering))
• Basics of Bayesian inference (Several examples,  Sampling algorithms)
• Basics of information theory (channel capacity, Fisher information, CRLB, experiment design)
• Experimental design.

Throughout the course, examples from astrophysics will be used.

## Learning Outcomes

Student will be able to:

1) Statistically model simple experiments in a solvable fashion.

2) Detect a signal from a complex family of possible signals in colored Gaussian noise.

3) Compute the expected performance of an experiment.

4) Measure a parameter using bayesian inference.

5) Have some experience in dynamic programming and state-space methods.

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