'True error' presentation slideshows

Machine Learning

Machine Learning. CSE 5095: Special Topics Course Boosting Nhan Nguyen Computer Science and Engineering Dept. Boosting. Method for converting rules of thumb into a prediction rule. Rule of thumb ? Method?. Binary Classification. X: set of all possible instances or examples.

1.09k views • 88 slides

Automatic Construction of Ab Initio Potential Energy Surfaces

451 views • 36 slides

609 views • 32 slides

901 views • 31 slides

Unit 4 - Molecular Genetics

498 views • 29 slides

Zagazig University Faculty of Engineering Phy . & Eng.Math . Dept . Engineering Mathematics (4)

Zagazig University Faculty of Engineering Phy . & Eng.Math . Dept . Engineering Mathematics (4) For Third Year (Electrical Department) (Power, Communications, and Computer Science) 2011-2012. Course Outline. Part 1 (Analytic Mathematics) Special Functions

471 views • 27 slides

Review Midterm Exam

Review Midterm Exam. Introduction. Definition of a learning system:. Computer Learning Algorithm. Class of Tasks T. Performance P. Experience E. Introduction. Elements in designing a learning system:. Define the knowledge to learn Define the representation of the target knowledge

342 views • 26 slides

Evaluating Hypothesis

Evaluating Hypothesis. Introduction Estimating Accuracy Sampling Theory and Confidence Intervals Differences in Error Comparing Learning Algorithms. Introduction. Given some training data a learning algorithm produces a hypothesis.

451 views • 25 slides

2.53k views • 25 slides

Trapezoidal Rule of Integration

Trapezoidal Rule of Integration. What is Integration. Integration:. The process of measuring the area under a function plotted on a graph. Where: f(x) is the integrand a= lower limit of integration b= upper limit of integration. Basis of Trapezoidal Rule.

779 views • 25 slides

Evaluating Hypothesis

Evaluating Hypothesis. Introduction Estimating Accuracy Sampling Theory and Confidence Intervals Differences in Error Comparing Learning Algorithms. Introduction. Given some training data a learning algorithm produces a hypothesis.

307 views • 25 slides

Evaluating optimization algorithms: bounds on the performance of optimizers on unseen problems

Evaluating optimization algorithms: bounds on the performance of optimizers on unseen problems. David Corne , Alan Reynolds. My wonderful new algorithm, Bee-inspired Orthogonal Local Linear Optimal Covariance K inetics Solver Beats CMA-ES on 7 out of 10 test problems !!.

349 views • 23 slides

Forecasting Forecast Error

Forecasting Forecast Error. Andy Moore & Kevin Smith UC Santa Cruz Hernan Arango Rutgers University. How can we predict forecast error ?. Ensemble methods. 0. t. Issues with ensemble methods: - how to choose the perturbations? - how large should the ensemble be?

358 views • 21 slides

Backward Divided Difference

Backward Divided Difference. Major: All Engineering Majors Authors: Autar Kaw, Sri Harsha Garapati http://numericalmethods.eng.usf.edu Transforming Numerical Methods Education for STEM Undergraduates. Backward Divided Difference http://numericalmethods.eng.usf.edu. Definition. . .

335 views • 17 slides

Central Divided Difference

Central Divided Difference. Major: All Engineering Majors Authors: Autar Kaw, Sri Harsha Garapati http://numericalmethods.eng.usf.edu Numerical Methods for STEM undergraduates. Definition. . . y. Slope at . f(x). x. Central Divided Difference. Example. Example :

461 views • 14 slides

Taylor Series Revisited

Taylor Series Revisited. Major: All Engineering Majors Authors: Autar Kaw, Luke Snyder http://numericalmethods.eng.usf.edu Transforming Numerical Methods Education for STEM Undergraduates. Taylor Series Revisited http://numericalmethods.eng.usf.edu. What is a Taylor series?.

364 views • 14 slides

CSI5388 Current Approaches to Evaluation

236 views • 13 slides

312 views • 12 slides

Why Do Stochastic Simulations?

214 views • 11 slides

Error

Error. Error. Error is the difference between a measured or calculated (reported) value and the true value. Engineers recognize that errors are present in their professional lives and must be able to: 1. Identify types of errors 2. Numerically express the magnitude of errors

325 views • 10 slides