My Story, my life, my identity - University of Alberta

Chaitin MY STORY, MY LIFE, MY IDENTITY International Journal of Qualitative Methods 3 (4) December 2004 2 Introduction In this article, I focus on using the life story method for …

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MATHEMATICAL METHODS OF PHYSICS I – 2014

MATHEMATICAL METHODS OF PHYSICS I – 2014 THOMAS CREUTZIG ABSTRACT.These are lecture notes in progress for Ma Ph 451 – Mathematical Physics I. The lecture starts with a brief discussion of linear algebra, Hilbert spaces and …

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BAYESIAN METHODS FOR CONTROL LOOP MONITORING …

BAYESIAN METHODS FOR CONTROL LOOP MONITORING AND DIAGNOSIS Biao Huang⁄;1 ⁄ Department of Chemical and Materials Engineering,University of Alberta, Edmonton, AB T6G 2G6, Canada Abstract: There exist many algorithms for control performance monitoring.

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CARDIOLOGY - University of Alberta

MCCQE 2002 Review Notes Cardiology – C3 BASIC CLINICAL CARDIAC EXAM. . . CONT. Precordial Inspection observe for apex beat, heaves, lifts Precordial Palpation apex - most lateral impulse PMI - point of maximal intensity location: normal at 5th intraclavicular space (ICS) at midclavicular line (≤10 cm from midline), lateral/inferior displaced in dilated cardiomyopathy (DCS)

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reference letters magazine - University of Alberta

5 Advice to potential referees A student, employee or colleague has asked you to write a reference letter but you have never written one before or you are not sure what the appropriate content is for a reference letter.

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Non-Linear & Logistic Regression

parameters – we are using maximum likelihood estimation • We can however calculate a pseudo R2 - Lots of options on how to do this, but the best for logistic regression appears to be McFadden's calculation Logistic Regression (a.k.a logit …

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C1: Electrical resistivity of different soil and rock types

phases (solid, liquid or gas). Thus to calculate the overall electrical resistivity of a rock, we must consider the individual resistivities and then compute the overall electrical resistivity. Consider a sandstone saturated with salt water. The grains are quartzite and have a …

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HYSYS User Guide - University of Alberta

v v Phone and E-mail Customer support is also available by phone, fax, and e-mail for customers who have a current support contract for their product(s).

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Chapter 5. Measurable Functions 1. Measurable Functions

Chapter 5. Measurable Functions §1. Measurable Functions Let X be a nonempty set, and let S be a σ-algebra of subsets of X. Then (X,S) is a measurable space. A subset E of X is said to be measurable if E ∈ S. In this chapter, we will consider functions from X to IR, where IR := IR∪{−∞}∪{+∞} is the set of extended real numbers.

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Materials Studio: Installation and Administration Guide

Thisisarecommendedminimumspecification.Greaterprocessingpower,speed,andmemoryare recommendedforanyheavydutyuse.Therearenographics-relatedrequirementsforrunning

Course basics CSE 190: Reinforcement Learning: An …

CSE 190: Reinforcement Learning: An Introduction CSE 190: Reinforcement Learning, Lecture 1 2 Course basics •The website for the class is linked off my homepage. •Grades will be based on programming assignments, homeworks, and class participation. •Homeworks will be turned in, but not graded, as wewill discuss the answers in class in small groups.

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A Brief Introduction to Reinforcement Learning

A Brief Introduction to Reinforcement Learning Jingwei Zhang zhang@informatik.uni-freiburg.de 1

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Reinforcement Learning: An Introduction - …

Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto "This is a highly intuitive and accessible introduction to the recent major developments in

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Part XIII Reinforcement Learning and Control

CS229Lecturenotes Andrew Ng Part XIII Reinforcement Learning and Control We now begin our study of reinforcement learning and adaptive control. In supervised learning, we saw algorithms that tried to make their outputs

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Introduction to Reinforcement Learning - Inria

2 Introduction to Reinforcement Learning Emotions theory: model on how the emotional process can bias the decision process [Damasio, 1994]. Dopamine and basal ganglia model: direct link with motor control and decision-making (e.g., [Doya, 1999]).

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Chapter 3: The Reinforcement Learning Problem An …

CSE 190: Reinforcement Learning, Lecture25 Policy at step t,! t: a mapping from states to action probabilities! t(s,a)= probability that a t=a when s t=s The Agent Learns a Policy •Reinforcement learning methods specify how the agent changes its policy as a result of experience.

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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning J. Zico Kolter Carnegie Mellon University 1. Agent interaction with environment Agent Environment States Rewardr Actiona 2. Of course, an oversimplification 3. Review: Markov decision process Recall a (discounted) Markov decision process ℳ=",#,$,%,&

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Introduction to reinforcement learning

Reinforcement learning o ers an abstraction to the problem of goal-directed learning from interaction. It proposes that the sensory, memory and control apparatus and

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REINFORCEMENT LEARNING: AN INTRODUCTION - IMTR

Reinforcement learning with tabular action-value function. Store in a table the current estimated values of each action. The true value of an action is the average reward received when this action

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Reinforcement Learning. Richard S. Sutton and Andrew G ...

Reinforcement learning is defined not by characterizing learning methods, but by characterizing a learning problem. Any method that is well suited to solving that

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