'Occam' presentation slideshows

Challenges to the Theory of Evolution

Challenges to the Theory of Evolution. Frog + time (instantaneous) -> Prince = Fairy Tale Frog + time (300 million yrs.) -> Prince = Science Dr. Gish, ICR. Dr. Heinz Lycklama heinz@osta.com www.osta.com/messages. Overview. What is (Macro) Evolution?

1.1k views • 94 slides

Bayesian models of inductive learning

Bayesian models of inductive learning. Tom Griffiths UC Berkeley. Charles Kemp CMU. Josh Tenenbaum MIT. What you will get out of this tutorial. Our view of what Bayesian models have to offer cognitive science

1.14k views • 93 slides

Optimization Problems for Polymorphisms of Single Nucleotides

Optimization Problems for Polymorphisms of Single Nucleotides. Polymorphisms. A polymorphism is a feature. Polymorphisms. A polymorphism is a feature - common to everybody. Polymorphisms. A polymorphism is a feature - common to everybody - not identical in everybody.

900 views • 74 slides

MS Identification

774 views • 61 slides

Concurrent issues in Java, Ada and Posix

631 views • 49 slides

Minimum Description Length An Adequate Syntactic Theory?

556 views • 43 slides

Mid-Term Exam

Mid-Term Exam. Next Wednesday Perceptrons Decision Trees SVMs Computational Learning Theory In class, closed book. PAC Learnability. Consider a concept class C defined over an instance space X (containing instances of length n ),

659 views • 40 slides

Learning intersections and thresholds of halfspaces

521 views • 37 slides

Genetic Programming

Genetic Programming A Brief Overview Darius Makaitis CSC 650 - Advanced Artificial Intelligence Creighton University What is Genetic Programming?

776 views • 32 slides

Genetic Programming

Genetic Programming. A Brief Overview. Darius Makaitis CSC 650 - Advanced Artificial Intelligence Creighton University. What is Genetic Programming?.

594 views • 32 slides

History of Astronomy

History of Astronomy. Arny, 3 rd Edition, Chapter 1. Which Theory is Correct?. Which Theory is Correct?. Classical theories (500 B.C. to 150 A.D.) Some ideas very complicated and wrong Others ideas simple and correct People just like you and me. Lesson overview.

463 views • 28 slides

First Geodetic Results from the Australasian VLBI Network

First Geodetic Results from the Australasian VLBI Network. Oleg Titov (1), Jim Lovell (2), Hiroshi Takeguchi (3), Jonathan Quick (4), Stanislav Shabala (2), Jamie McCallum (2), Jing Sun (5), John Dawson (1), Aletha de Witt (4), Cormac Reynolds (6), Hayley Bignall (6),

318 views • 25 slides

CAUSES and EFFECTS

CAUSES and EFFECTS. Part 1: THE RHETORICAL STRATEGY. BACKGROUND. BACKGROUND: To reinforce the complexity & non-linearity Of Causality & Consequence To rectify certain misconceptions concerning Causality & Consequence. 2. COMPLICATED. BACKGROUND. NO OVERSIMPLIFICATION :

363 views • 22 slides

Zipf !

Zipf !. Some talking points Questions? Complaints? TylerS at Stanford dot edu. The Principle of Least Effort.

403 views • 18 slides

307 views • 17 slides

LECTURE 18: DECISION TREES

LECTURE 18: DECISION TREES. Objectives: Tree-Growing Via CART Splitting, Stopping and Pruning Attributes Node Impurity Priors and Costs Resources: DHS: Chapter 8 WIKI : Definitions AAAI: Decision Trees AM: Data Mining DTDM: Resources JB: Examples ISIP: Software. Audio:. URL:. Overview.

227 views • 14 slides

Regression

Regression. Eric Feigelson Lecture and R tutorial Arcetri Observatory April 2014. Regression vs. density estimation.

223 views • 11 slides

LECTURE 12: PARAMETRIC AND MINIMUM VARIANCE SPECTRAL ESTIMATION. Objectives: AR Spectral Estimation MA Spectral Estimation ARMA Spectral Estimation Minimum Variance Estimation

376 views • 11 slides

Wireshark in the Large Enterprise June 16, 2010 Hansang Bae

162 views • 9 slides

LECTURE 21: FOUNDATIONS OF MACHINE LEARNING

196 views • 5 slides