complex128 ) f_low = fshift f_ishift_low = np. abs ( img_back_high ), cmap = 'gray' ) plt. imshow ( magnitude_spectrum, cmap = 'gray' ) plt. fftshift ( f ) magnitude_spectrum = 20 * np. imread ( '././CSC420 Intro to Image Understanding/assets/yurina.jpg', cv2. figure ( figsize = ( 12, 10 )) img = cv2. Random Variables and Single Variable DistributionsĪutoregressive (AR) and Moving Average (MA) modelĪutoregressive Integrated Moving Average (ARIMA) modelįorecasting and Transfer Function Noise Model Log-linear Regression (Poisson Regression)įactor independence and Contingency Table STA303/STA1002 Methods of Data Analysis II

Unconstrainted Optimization on Calulus of VariationsĮxamples: Unconstrainted Optimization on Calulus of VariationsĬonstrainted Optimization on Calulus of VariationsĮxamples: Constrainted Optimization on Calulus of Variations Inequality Constrained Finite Dimension OptimizationĮxamples: Inequality Constrained Finite Dimension OptimizationĬonjugate Directions and Conjugate GradientsĮxamples: Conjugate Directions and Conjugate Gradients Unconstrained Finite Dimensional OptimizationĮxamples: Unconstrained Finite Dimensional OptimizationĮquality Constrained Finite Dimension OptimizationĮxamples: Equality Constrained Finite Dimension Optimization More Examples of Surface Regularity and Smoothness OpenMP - Synchronizations and Performance Profilingĭistributed Memory Model and MPI IntroductionĪpproxmation Errors and Conditioning, StabilityĬSC373 Algorithms Design, Analysis and ComplexityĬSC265 Enriched Data Structures and Analysis Joint distributions with Graphical modelsĭimension Reduction - PCA and Auto EncodersĬS294-082 Experimental Design for Machine Learning on Multimedia Data Fourier Transformation and Convolution TheoremĬSC412/CSC2506 Probabilistic Learning and Reasoning

Convolution fourier transform