Course Identification

Data analysis and signal processing
20191022

Lecturers and Teaching Assistants

Prof. Eran Ofek, Prof. Eilam Gross
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Course Schedule and Location

2019
Second Semester
Tuesday, 09:15 - 12:00, WSoS, Rm B
26/03/2019

Field of Study, Course Type and Credit Points

Physical Sciences: Lecture; Elective; 3.00 points
Chemical Sciences: Lecture; Elective; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; 3.00 points

Comments

N/A

Prerequisites

Suggested: "Data processing" course

Restrictions

40

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Pass / Fail

Grade Breakdown (in %)

50%
50%

Evaluation Type

Final assignment

Scheduled date 1

09/08/2019
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-
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Estimated Weekly Independent Workload (in hours)

2

Syllabus

  • Hypothesis testing (NP, UMP)
  • matched filter, optimal weighting
  • Fourier Analysis (convolution theorem, power spectrum, autocorrelation, FFT, Linear systems, basic signal processing, noise whitening)
  • Dynamic programming (concept + algorithms (e.g., Radon Transform))
  • Robust statistics & Non-Linear statistis (MCMC,Bootstrap, Jacknife and resampling, sampling algorithms)
  • Information theory (channel capacity, Fisher information, CRLB, experiment design, prunning)
  • Linear Algebra algorithms (inverting a matrix, SVD, PCA, Solving a sparse system of equations, Fast inversion, conjugate gradient)
  • Optimization (convex, Gradient Descent, Newton-Raphson, Gauss-Newton)
  • Time series analysis (e.g., power spectrum, cross-correlation, auto-regression)

Learning Outcomes

Upon successful completion of this course, students twill be able to:

Demonstrate practical experience with data analysis, signal processing, and Fourier transform.

Reading List

N/A

Website

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