Coursera - Computational Methods for Data Analysis

Posted By: ParRus

Date: 13 Nov 2016 18:09:26

Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression.

Content:

01 Computational Methods Course Overview
02 Introduction to the Course
03 MATLAB Usage in Course
04 Week 1 - Lecture 1 - Time-Frequency Analysis - Fourier and Wavelet Transforms
05 Week 1 - Lecture 2 - Radar Detection and Filtering
06 Week 1 - Lecture 3 - Radar Detection and Averaging
07 Week 2 - Lecture 4 - The Windowed Fourier Transform
08 Week 2 - Lecture 5 - The Wavelet Transform
09 Week 2 - Lecture 6 - The Wavelet Basis and Multi-Resolution Analysis
10 Week 3 - Lecture 7 - Spectrograms and the Gabor transforms in MATLAB
11 Week 3 - Lecture 8 - MATLAB Filter Design and Wavelet Toolboxes
12 Week 4 - Lecture 9 - Image processing and analysis
13 Week 4 - Lecture 10 - Linear Filtering for Image Denoising
14 Week 4 - Lecture 11 - Diffusion and Image Processing
15 Week 5 - Lecture 12 - Linear Algebra and the Singular Value Decomposition
16 Week 5 - Lecture 13 - The SVD in Broader Context
17 Week 5 - Lecture 14 - Principal Component Analysis
18 Week 6 - Lecture 15 - Principal Component Analysis SVD diagonlization
19 Week 6 - Lecture 16 - Principal Component and Proper Orthogonal Modes
20 Week 7 - Lecture 17 - Independent Component Analysis
21 Week 7 - Lecture 18 - Image Separation with the SVD
22 Week 7 - Lecture 19 - Implementing an Image Separation Algorithm
23 Week 8 - Lecture 20 - Image Recognition
24 Week 8 - Lecture 21 - The SVD and linear discrimination analysis
25 Week 8 - Lecture 22 - Implementing the catdog regognition algorithm
26 Week 9 - Lecture 23 - Basics of Compressive Sensing
27 Week 9 - Lecture 24 - Signal Reconstruction and Circumventing Nyquist
28 Week 9 - Lecture 25 - Data Image Reconstruction from Sparse Sampling
29 Week 10 - Lecture 26 - Dimensionality Reduction for Partial Differential Equations
30 Week 10 - Lecture 27 - PDE dynamics in the right best basis
31 Week 10 - Lecture 28 - Global normal forms of bifurcation structures in PDEs

About the Instructor(s)

J. Nathan Kutz
PhD, Applied Mathematics, Northwestern University
J. Nathan Kutz specializes in a unified approach to applied mathematics including modeling, computation and analysis. His current focus is phenomena in dimensionality reduction and data-analysis techniques for complex systems. This includes work in laser dynamics and modelocking in fiber lasers, neuro-sensory systems and theoretical neuroscience, and gesture recognition algorithms for portable electronic devices. Kutz has authored numerous scientific articles on these subjects as well as segments of books devoted to his area of expertise.

also You can watch my other last: Coursera-posts

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