'Approximately correct' presentation slideshows

The Causes of Variation

The Causes of Variation. Lindon Eaves and Tim York Boulder, CO March 2001. One Issue (Among Many!). Identifying genes that cause complex diseases and genes that contribute to variation in quantitative traits. Quantitative Trait Locus (QTL).

898 views • 64 slides

575 views • 46 slides

Two Independent Populations (Chapter 6)

Two Independent Populations (Chapter 6). Develop a confidence interval for the difference in means between two independent normal populations . Develop a statistical test for the difference in means between two independent normal populations , assuming: equal population variances;

595 views • 36 slides

Setting Key performance indicators to monitor progress

Setting Key performance indicators to monitor progress. What are indicators Types of indicators Indicators at different levels Qualities of a good indicator Setting up indicators. Definition of an Indicator.

1.23k views • 30 slides

Lecture 14

Lecture 14. Learning Inductive inference Probably approximately correct learning. What is learning?. Key point: all learning can be seen as learning the representation of a function. Will become clearer with more examples! Example representations: propositional if-then rules

342 views • 27 slides

Lecture 14

Lecture 14. Learning Inductive inference Probably approximately correct learning. What is learning?. Key point: all learning can be seen as learning the representation of a function. Will become clearer with more examples! Example representations: propositional if-then rules

626 views • 25 slides

Writing Equations in Slope-Intercept Form

619 views • 22 slides

PAC Learning

PAC Learning. 8/5/2005. purpose. Effort to understand negative selection algorithm from totally different aspects Statistics Machine learning What is machine learning, in a very informal way?

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Machine Learning

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Greeks By A.V. Vedpuriswar

Greeks By A.V. Vedpuriswar. October 24, 2010. Introduction. Greeks help us to measure the risk associated with derivative positions. Greeks also come in handy when we do local valuation of instruments. But Greeks are not useful to get an aggregated view of risk. Delta.

429 views • 15 slides

Characterization of Kernels

Consider a finite space. is a symmetric function on. and. Characterization of Kernels. Motivation in Finite Input Space. Let. be a matrix defined as following:. There is an orthogonal matrix. such that:. Characterization of Kernels. Be Positive Semi-definite. Assume:. Let. where.

173 views • 9 slides

ANOVA

ANOVA. One Way Analysis of Variance. ANOVA. Purpose: To assess whether there are differences between means of multiple groups. ANOVA provides evidence. We compare variation among the means of the groups with the variation within groups. Hence the name Analysis of Variance. ANOVA HYPOTHESES.

2.04k views • 8 slides

Can Inductive Learning Work?

Inductive hypothesis h. Training set D. p ( x ): probability that example x is picked from X. size |H|. size m. -. -. +. -. +. -. -. -. -. +. +. +. +. -. -. +. +. +. +. -. -. -. +. +. Hypothesis space H. Example set X.

192 views • 8 slides