PPT-Models of

behaviour management Lecture 3 August 11 2014 Ray Handley EDPP302 Risk and behaviour management This week Behaviourist principles Overview of teacher centred or controlling approaches. Designing Instruction for 21. st. Century Learners. Kilbane, C. R., & Milman, N. B. (2013. ). . Teaching . models: Designing instruction for 21. st. century learners. . Boston, MA: Pearson.. About the presenters. Eigenvalues. (9.1) Leslie Matrix Models. (9.2) Long Term Growth Rate (. Eigenvalues. ). (9.3) Long Term Population Structure (Corresponding Eigenvectors). Introduction. In the models presented and discussed in Chapters 6, 7, and 8, nothing is created or destroyed:. CAS Spring Meeting 2015. R. 2. Richard Rosengarten. May 18, 2015. Collective . Risk Model (CRM) for multiple lines of business with correlation. . Well-Trodden . Ground:. Wang. Meyers and Collaborators . Chapter 14 . The pinhole camera. Structure. Pinhole camera model. Three geometric problems. Homogeneous coordinates. Solving the problems. Exterior orientation problem. Camera calibration. 3D reconstruction. Giorgio Busoni. 1. Based. on. : . arXiv:1409.2893 (and 1307.2253, 1402.1275, . 1405.3101. , 1402.2285) . Oxford, 27 September 2014. Outline. Problems with EFT approach in Mono-X searches. From EFT to Simplified models. Jure Žabkar. Exploration and Curiosity in Robot Learning and Inference. , . DAGSTUHL, March 2011. joint work with xpero partners. problem. “. How should. . a robot. . choose. . its. . actions. Ovidiu P. â. rvu. , PhD student. Department of . Computer Science. Supervisors: Professors . David Gilbert. and . Nigel Saunders. Why?. 2. Predicted. behaviour. Simulations. Natural. biosystem. Computational. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. Chapter 5 . The Normal Distribution. Univariate. Normal Distribution. For short we write:. Univariate. normal distribution describes single continuous variable.. Takes 2 parameters . m. and . s. 2. Jure Žabkar. Exploration and Curiosity in Robot Learning and Inference. , . DAGSTUHL, March 2011. joint work with xpero partners. problem. “. How should. . a robot. . choose. . its. . actions. Instructor: Paul Tarau, based on . Rada. . Mihalcea’s. original slides. Note. : some of the material in this slide set was adapted from an NLP course taught by Bonnie Dorr at Univ. of Maryland. Language Models. Lecture 06. Thomas Herring. tah@mit.edu. . Issues in GPS Error Analysis. What are the sources of the errors ?. How much of the error can we remove by better modeling ?. Do we have enough information to infer the uncertainties from the data ?. Models for. Count Data. Doctor Visits. Basic Model for Counts of Events. E.g., Visits to site, number of purchases, number of doctor visits. Regression approach. Quantitative outcome measured. Discrete variable, model probabilities. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature).