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Principal Component Analysis

Principal Component Analysis

Principal Component Analysis. Mark Stamp. Intro. Consider linear algebra techniques Can apply directly to binaries No need for a costly disassembly step Reveals file structure But may not be obvious structure Theory is challenging Training is somewhat complex Scoring is fast and easy.

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1.27k views • 72 slides
Last lecture summary

Last lecture summary

Last lecture summary. Basic terminology. tasks classification regression learner, algorithm each has one or several parameters influencing its behavior model one concrete combination of learner and parameters tune the parameters using the training set

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640 views • 55 slides
Chapter 4

Chapter 4

Chapter 4. Exercise 1. The 95% CI represents a range of values that contains a population parameter with a 0.95 probability. This range is determined by the values that correspond to the 0.025 and 0.975 quantiles of the sampling distribution of the sample statistic. . Exercise 2.

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581 views • 48 slides
2D/3D Shape Manipulation, 3D Printing

2D/3D Shape Manipulation, 3D Printing

2D/3D Shape Manipulation, 3D Printing. Normal Estimation in Point Clouds. Slides from Olga Sorkine. Implicit Surface Reconstruction. Implicit function from point clouds Need consistently oriented normals. 0. < 0. > 0. Normal Estimation.

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706 views • 37 slides
Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU

Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU

Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU. Tao Cui (Presenter) Franz Franchetti Dept. of ECE. Carnegie Mellon University Pittsburgh PA tcui@ece.cmu.edu. This work is supported by NSF 0931978 & 1116802. Smart Grids. Image by Dr. M.Sanchez. Smart Grids.

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450 views • 25 slides
PSY 250

PSY 250

PSY 250. Chapter 8: Between Subjects Designs. Experimental Design: The Basic Building Blocks. Experimental design The general plan for selecting participants, assigning participants to experimental conditions, controlling extraneous variables, and gathering data.

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340 views • 20 slides
Limitations of Global FDR

Limitations of Global FDR

Limitations of Global FDR. Less likely to be false positive. More likely to be false positive. Global FDR rejection region. threshold. 0. 1. Single hypothesis testing P -values. Global FDR method cannot associate the corrected significance with each hypothesis testing

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197 views • 7 slides

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