CS221 / Autumn 2017 / Liang & Ermon. Lecture 19: Conclusion

Applied Machine Learning, Lecture 11: Ethical and legal considerations; domain effects and domain adaptation

Applied Machine Learning, Lecture 11: Ethical and legal considerations; domain effects and domain adaptation Richard Johansson including some slides borrowed from Barbara Plank overview introduction bias

ERA: Architectures for Inference

ERA: Architectures for Inference Dan Hammerstrom Electrical And Computer Engineering 7/28/09 1 Intelligent Computing In spite of the transistor bounty of Moore s law, there is a large class of problems

Cognitive Modeling. Lecture 12: Bayesian Inference. Sharon Goldwater. School of Informatics University of Edinburgh

Cognitive Modeling Lecture 12: Bayesian Inference Sharon Goldwater School of Informatics University of Edinburgh sgwater@inf.ed.ac.uk February 18, 20 Sharon Goldwater Cognitive Modeling 1 1 Prediction

CS 4365: Artificial Intelligence Recap. Vibhav Gogate

CS 4365: Artificial Intelligence Recap Vibhav Gogate Exam Topics Search BFS, DFS, UCS, A* (tree and graph) Completeness and Optimality Heuristics: admissibility and consistency CSPs Constraint graphs,

Bayesian Inference Bayes Laplace

Bayesian Inference Bayes Laplace Course objective The aim of this course is to introduce the modern approach to Bayesian statistics, emphasizing the computational aspects and the differences between the

Artificial intelligence and judicial systems: The so-called predictive justice. 20 April

Artificial intelligence and judicial systems: The so-called predictive justice 20 April 2018 1 Context The use of so-called artificielle intelligence received renewed interest over the past years.. Stakes

On Algorithms and Fairness

On Algorithms and Fairness Jon Kleinberg Cornell University Includes joint work with Sendhil Mullainathan, Manish Raghavan, and Maithra Raghu Forming Estimates of Future Performance Estimating probability

Kai-Wei Chang UCLA. What It Takes to Control Societal Bias in Natural Language Processing. References:

What It Takes to Control Societal Bias in Natural Language Processing Kai-Wei Chang UCLA References: http://kwchang.net Kai-Wei Chang (kwchang.net/talks/sp.html) 1 A father and son get in a car crash and

Intelligent Machines That Act Rationally. Hang Li Toutiao AI Lab

Intelligent Machines That Act Rationally Hang Li Toutiao AI Lab Four Definitions of Artificial Intelligence Building intelligent machines (i.e., intelligent computers) Thinking humanly Acting humanly Thinking

Signal Detection Theory and Bayesian Modeling

Signal Detection Theory and Bayesian Modeling COGS 202: Computational Modeling of Cognition Omar Shanta, Shuai Tang, Gautam Reddy, Reina Mizrahi, Mehul Shah Detection Theory and Psychophysics: A Review

Burn-in, bias, and the rationality of anchoring

Burn-in, bias, and the rationality of anchoring Falk Lieder Translational Neuromodeling Unit ETH and University of Zurich lieder@biomed.ee.ethz.ch Thomas L. Griffiths Psychology Department University of

Intelligent Machines That Act Rationally. Hang Li Bytedance AI Lab

Intelligent Machines That Act Rationally Hang Li Bytedance AI Lab Four Definitions of Artificial Intelligence Building intelligent machines (i.e., intelligent computers) Thinking humanly Acting humanly

Generative Adversarial Networks.

Generative Adversarial Networks www.cs.wisc.edu/~page/cs760/ Goals for the lecture you should understand the following concepts Nash equilibrium Minimax game Generative adversarial network Prisoners Dilemma

Putting Context into. Vision. September 15, Derek Hoiem

Putting Context into Vision Derek Hoiem September 15, 2004 Questions to Answer What is context? How is context used in human vision? How is context currently used in computer vision? Conclusions Context

Vision as Bayesian inference: analysis by synthesis?

Vision as Bayesian inference: analysis by synthesis? Schwarz Andreas, Wiesner Thomas 1 / 70 Outline Introduction Motivation Problem Description Bayesian Formulation Generative Models Letters, Text Faces

CS343: Artificial Intelligence

CS343: Artificial Intelligence Introduction: Part 2 Prof. Scott Niekum University of Texas at Austin [Based on slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All materials

A Gendered Perspective on Artificial Intelligence

A Gendered Perspective on Artificial Intelligence Smriti Parsheera National Institute of Public Finance & Policy smriti.parsheera@gmail.com Overview The origins of AI and its gender dimensions The questions:

Event Classification and Relationship Labeling in Affiliation Networks

Event Classification and Relationship Labeling in Affiliation Networks Abstract Many domains are best described as an affiliation network in which there are entities such as actors, events and organizations

Bias In, Bias Out. Adventures in Algorithmic Fairness. Sandy Mayson November 3, 2016

Bias In, Bias Out Adventures in Algorithmic Fairness Sandy Mayson November 3, 2016 1 [B]lacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend. It makes the opposite

Course Departmental Prefix and #: Title: Course Departmental Prefix and #: Title: Course Departmental Prefix and #: Title:

Psychology Major Tracking Sheet AB Requirements Eleven (11) courses in psychology are required for the major. The major is devised to provide breadth and depth, a small group course in psychology, and

CISC453 Winter Probabilistic Reasoning Part B: AIMA3e Ch

CISC453 Winter 2010 Probabilistic Reasoning Part B: AIMA3e Ch 14.5-14.8 Overview 2 a roundup of approaches from AIMA3e 14.5-14.8 14.5 a survey of approximate methods alternatives to the direct computing

Probabilistically Estimating Backbones and Variable Bias: Experimental Overview

Probabilistically Estimating Backbones and Variable Bias: Experimental Overview Eric I. Hsu, Christian J. Muise, J. Christopher Beck, and Sheila A. McIlraith Department of Computer Science, University

Pythia WEB ENABLED TIMED INFLUENCE NET MODELING TOOL SAL. Lee W. Wagenhals Alexander H. Levis

Pythia WEB ENABLED TIMED INFLUENCE NET MODELING TOOL Lee W. Wagenhals Alexander H. Levis ,@gmu.edu Adversary Behavioral Modeling Maxwell AFB, Montgomery AL March 8-9, 2007 1 Outline Pythia

Computational Cognitive Neuroscience

Computational Cognitive Neuroscience Computational Cognitive Neuroscience Computational Cognitive Neuroscience *Computer vision, *Pattern recognition, *Classification, *Picking the relevant information

Marcus Hutter Canberra, ACT, 0200, Australia

Marcus Hutter Canberra, ACT, 0200, Australia http://www.hutter1.net/ Australian National University Abstract The approaches to Artificial Intelligence (AI) in the last century may be labelled as (a) trying

Why did the network make this prediction?

Why did the network make this prediction? Ankur Taly (Google Inc.) Joint work with Mukund Sundararajan and Qiqi Yan Some Deep Learning Successes Source: https://www.cbsinsights.com Deep Neural Networks

Supervised Learner for the Prediction of Hi-C Interaction Counts and Determination of Influential Features. Tyler Yue Lab

Supervised Learner for the Prediction of Hi-C Interaction Counts and Determination of Influential Features Tyler Derr @ Yue Lab tsd5037@psu.edu Background Hi-C is a chromosome conformation capture (3C)

Exploring the Influence of Particle Filter Parameters on Order Effects in Causal Learning

Exploring the Influence of Particle Filter Parameters on Order Effects in Causal Learning Joshua T. Abbott (joshua.abbott@berkeley.edu) Thomas L. Griffiths (tom griffiths@berkeley.edu) Department of Psychology,

Minority Report: ML Fairness in Criminality Prediction

Minority Report: ML Fairness in Criminality Prediction Dominick Lim djlim@stanford.edu Torin Rudeen torinmr@stanford.edu 1. Introduction 1.1. Motivation Machine learning is used more and more to make decisions

CS6501: Deep Learning for Visual Recognition. GenerativeAdversarial Networks (GANs)

CS6501: Deep Learning for Visual Recognition GenerativeAdversarial Networks (GANs) Today s Class Adversarial Examples Input Optimization Generative Adversarial Networks (GANs) Conditional GANs Style-Transfer

PART - A 1. Define Artificial Intelligence formulated by Haugeland. The exciting new effort to make computers think machines with minds in the full and literal sense. 2. Define Artificial Intelligence

Decisions and Dependence in Influence Diagrams

JMLR: Workshop and Conference Proceedings vol 52, 462-473, 2016 PGM 2016 Decisions and Dependence in Influence Diagrams Ross D. hachter Department of Management cience and Engineering tanford University

How do Categories Work?

Presentations Logistics Think about what you want to do Thursday we ll informally sign up, see if we can reach consensus. Topics Linear representations of classes Non-linear representations of classes

Lecture 3: Bayesian Networks 1

Lecture 3: Bayesian Networks 1 Jingpeng Li 1 Content Reminder from previous lecture: Bayes theorem Bayesian networks Why are they currently interesting? Detailed example from medical diagnostics Bayesian

High-level Vision. Bernd Neumann Slides for the course in WS 2004/05. Faculty of Informatics Hamburg University Germany

High-level Vision Bernd Neumann Slides for the course in WS 2004/05 Faculty of Informatics Hamburg University Germany neumann@informatik.uni-hamburg.de http://kogs-www.informatik.uni-hamburg.de 1 Contents

Visual semantics: image elements. Symbols Objects People Poses

Visible Partisanship Polmeth XXXIII, Rice University, July 22, 2016 Convolutional Neural Networks for the Analysis of Political Images L. Jason Anastasopoulos ljanastas@uga.edu (University of Georgia,

Learning and Adaptive Behavior, Part II

Learning and Adaptive Behavior, Part II April 12, 2007 The man who sets out to carry a cat by its tail learns something that will always be useful and which will never grow dim or doubtful. -- Mark Twain

Introduction to Machine Learning. Katherine Heller Deep Learning Summer School 2018

Introduction to Machine Learning Katherine Heller Deep Learning Summer School 2018 Outline Kinds of machine learning Linear regression Regularization Bayesian methods Logistic Regression Why we do this

Convolutional Neural Networks for Text Classification

Convolutional Neural Networks for Text Classification Sebastian Sierra MindLab Research Group July 1, 2016 ebastian Sierra (MindLab Research Group) NLP Summer Class July 1, 2016 1 / 32 Outline 1 What is

Hierarchical Convolutional Features for Visual Tracking

Hierarchical Convolutional Features for Visual Tracking Chao Ma Jia-Bin Huang Xiaokang Yang Ming-Husan Yang SJTU UIUC SJTU UC Merced ICCV 2015 Background Given the initial state (position and scale), estimate

Applications with Bayesian Approach

Applications with Bayesian Approach Feng Li feng.li@cufe.edu.cn School of Statistics and Mathematics Central University of Finance and Economics Outline 1 Missing Data in Longitudinal Studies 2 FMRI Analysis

MS&E 226: Small Data

MS&E 226: Small Data Lecture 10: Introduction to inference (v2) Ramesh Johari ramesh.johari@stanford.edu 1 / 17 What is inference? 2 / 17 Where did our data come from? Recall our sample is: Y, the vector

Bayesian Machine Learning for Decoding the Brain

for Decoding the Brain Radboud University Nijmegen, The Netherlands Institute for Computing and Information Sciences Radboud University Nijmegen, The Netherlands June 10, IWANN 2011, Torremolinos Faculty

Testing Bias Prevention Techniques on Recidivism Risk Models

Testing Bias Prevention Techniques on Recidivism Risk Models Claudia McKenzie, Mathematical and Computational Science, Stanford University, claudi10@stanford.edu 1 Introduction Risk assessment algorithms

What is Artificial Intelligence? A definition of Artificial Intelligence. Systems that act like humans. Notes

What is? It is a young area of science (1956) Its goals are what we consider Intelligent behaviour There are many approaches from different points of view It has received influence from very diverse areas

BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS

Sara Garofalo Department of Psychiatry, University of Cambridge BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS Overview Bayesian VS classical (NHST or Frequentist) statistical approaches Theoretical issues

Risk Assessment Update: ARREST SCALES February 28, 2018 DRAFT

SUMMARY: In December 2017 the Commission voted to replace number of prior convictions with number of prior arrests as a predictor in the risk assessment scales. Over the past months staff has prepared

Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford

Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford

Probabilistic Models in Cognitive Science and Artificial Intelligence

Probabilistic Models in Cognitive Science and Artificial Intelligence Very Brief History of Cog Sci and AI 1950 s-1980 s Symbolic models of cognition von Neumann computer architecture as metaphor 1980

Just Machine Learning

Just Machine Learning tina@eliassi.org Tina Eliassi-Rad @tinaeliassi http://eliassi.org/safra17.pdf What is machine learning? https://xkcd.com/1838/ Machine learning emerged from AI Economics and Organizational

Introduction and Historical Background. August 22, 2007

1 Cognitive Bases of Behavior Introduction and Historical Background August 22, 2007 2 Cognitive Psychology Concerned with full range of psychological processes from sensation to knowledge representation

Evaluating Risk Assessment: Finding a Methodology that Supports your Agenda There are few issues facing criminal justice decision makers generating

Evaluating Risk Assessment: Finding a Methodology that Supports your Agenda There are few issues facing criminal justice decision makers generating more interest than fairness and bias with risk assessments.

State Estimation: Particle Filter

State Estimation: Particle Filter Daniel Seliger HAUPT-/ BACHELOR- SEMINAR ADAPTIVE 28.06.2012 SYSTEME PST PROF. DR. WIRSING 14. JUNI 2009 VORNAME NAME Overview 1. Repitition: Bayesian Filtering 2. Particle

A Bayesian Network Model for Analysis of the Factors Affecting Crime Risk

A Bayesian Network Model for Analysis of the Factors Affecting Crime Risk ROONGRASAMEE BOONDAO, VATCHARAPORN ESICHAIKUL and NITIN KUMAR TRIPATHI School of Advanced Technologies Asian Institute of Technology

Improving the Interpretability of DEMUD on Image Data Sets

Improving the Interpretability of DEMUD on Image Data Sets Jake Lee, Jet Propulsion Laboratory, California Institute of Technology & Columbia University, CS 19 Intern under Kiri Wagstaff Summer 2018 Government

Between-word regressions as part of rational reading

Between-word regressions as part of rational reading Klinton Bicknell & Roger Levy UC San Diego CUNY 2010: New York Bicknell & Levy (UC San Diego) Regressions as rational reading CUNY 2010 1 / 23 Introduction

HHS Public Access Author manuscript Trends Cogn Sci. Author manuscript; available in PMC 2018 May 01.

Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline Thomas Naselaris 1, Danielle S. Bassett 2, Alyson K. Fletcher 3, Konrad Kording 4, Nikolaus Kriegeskorte 5, Hendrikje Nienborg

P. RICHARD HAHN. Research areas. Employment. Education. Research papers

P. RICHARD HAHN Arizona State University email: prhahn@asu.edu Research areas Bayesian methods, causal inference, foundations of statistics, nonlinear regression, Monte Carlo methods, applications to social

Introduction to Deep Reinforcement Learning and Control

Carnegie Mellon School of Computer Science Deep Reinforcement Learning and Control Introduction to Deep Reinforcement Learning and Control Lecture 1, CMU 10703 Katerina Fragkiadaki Logistics 3 assignments

Rethinking Cognitive Architecture!

Rethinking Cognitive Architecture! Reconciling Uniformity and Diversity via Graphical Models! Paul Rosenbloom!!! 1/25/2010! Department of Computer Science &! Institute for Creative Technologies! The projects

Kelvin Chan Feb 10, 2015

Underestimation of Variance of Predicted Mean Health Utilities Derived from Multi- Attribute Utility Instruments: The Use of Multiple Imputation as a Potential Solution. Kelvin Chan Feb 10, 2015 Outline

Fodor on Functionalism EMILY HULL

Fodor on Functionalism EMILY HULL Deficiencies in Other Theories Dualism Failure to account for mental causation Immaterial substance cannot cause physical events If mental processes were a different substance

A Vision-based Affective Computing System. Jieyu Zhao Ningbo University, China

A Vision-based Affective Computing System Jieyu Zhao Ningbo University, China Outline Affective Computing A Dynamic 3D Morphable Model Facial Expression Recognition Probabilistic Graphical Models Some

arxiv: v1 [stat.ml] 23 Jan 2017

Learning what to look in chest X-rays with a recurrent visual attention model arxiv:1701.06452v1 [stat.ml] 23 Jan 2017 Petros-Pavlos Ypsilantis Department of Biomedical Engineering King s College London

The University of Tokyo, NVAIL Partner Yoshitaka Ushiku

Recognize, Describe, and Generate: Introduction of Recent Work at MIL The University of Tokyo, NVAIL Partner Yoshitaka Ushiku MIL: Machine Intelligence Laboratory Beyond Human Intelligence Based on Cyber-Physical

A HMM-based Pre-training Approach for Sequential Data

A HMM-based Pre-training Approach for Sequential Data Luca Pasa 1, Alberto Testolin 2, Alessandro Sperduti 1 1- Department of Mathematics 2- Department of Developmental Psychology and Socialisation University

Individual Differences in Attention During Category Learning

Individual Differences in Attention During Category Learning Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, 35 Social Sciences Plaza A University of California, Irvine, CA 92697-5 USA

Fairness in Machine Learning and Its Causal Aspects

Fairness in Machine Learning and Its Causal Aspects Ricardo Silva University College London and The Alan Turing Institute ricardo@stats.ucl.ac.uk With joint work by Matt Kusner, Joshua Loftus and Chris

What is AI? The science of making machines that:

What is AI? The science of making machines that: Think like humans Think rationally Act like humans Act rationally Thinking Like Humans? The cognitive science approach: 1960s ``cognitive revolution'':

Making fair decisions with algorithms

Making fair decisions with algorithms Sam Corbett-Davies with Emma Pierson, Avi Feller, Aziz Huq, and Sharad Goel How do we identify bias in algorithmic decisions? Idea: consider how researchers have identified

in Engineering Prof. Dr. Michael Havbro Faber ETH Zurich, Switzerland Swiss Federal Institute of Technology

Risk and Safety in Engineering Prof. Dr. Michael Havbro Faber ETH Zurich, Switzerland Contents of Today's Lecture Introduction to Bayesian Probabilistic Nets (BPN) Causality as a support in reasoning Basic

Introduction to Computational Neuroscience

Introduction to Computational Neuroscience Lecture 5: Data analysis II Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis 5 Data analysis II 6 Single

Professional Skills in Computer Science Lecture 8: Induction (2)

Professional Skills in Computer Science Lecture 8: Induction (2) Ullrich Hustadt Department of Computer Science School of Electrical Engineering, Electronics, and Computer Science University of Liverpool

Definitions. The science of making machines that: This slide deck courtesy of Dan Klein at UC Berkeley

Definitions The science of making machines that: Think like humans Think rationally Act like humans Act rationally This slide deck courtesy of Dan Klein at UC Berkeley Acting Like Humans? Turing (1950)

KECERDASAN BUATAN 3. By Sirait. Hasanuddin Sirait, MT

KECERDASAN BUATAN 3 By @Ir.Hasanuddin@ Sirait Why study AI Cognitive Science: As a way to understand how natural minds and mental phenomena work e.g., visual perception, memory, learning, language, etc.

Deep Networks and Beyond. Alan Yuille Bloomberg Distinguished Professor Depts. Cognitive Science and Computer Science Johns Hopkins University

Deep Networks and Beyond Alan Yuille Bloomberg Distinguished Professor Depts. Cognitive Science and Computer Science Johns Hopkins University Artificial Intelligence versus Human Intelligence Understanding

University of Cambridge Engineering Part IB Information Engineering Elective

University of Cambridge Engineering Part IB Information Engineering Elective Paper 8: Image Searching and Modelling Using Machine Learning Handout 1: Introduction to Artificial Neural Networks Roberto

Interpreting Deep Neural Networks and their Predictions

Fraunhofer Image Processing Heinrich Hertz Institute Interpreting Deep Neural Networks and their Predictions Wojciech Samek ML Group, Fraunhofer HHI (joint work with S. Lapuschkin, A. Binder, G. Montavon,

First Problem Set: Answers, Discussion and Background

First Problem Set: Answers, Discussion and Background Part I. Intuition Concerning Probability Do these problems individually Answer the following questions based upon your intuitive understanding about

Introduction to Medical Computing

CS 2124-2125 Introduction to Medical Computing Stephen M. Watt Artificial Intelligence in Medicine Notes by Jacquelyne Forgette, March 2012. University of Western Ontario CS 2125. Stephen M. Watt AIM Artificial

Confluence: Conformity Influence in Large Social Networks

Confluence: Conformity Influence in Large Social Networks Jie Tang *, Sen Wu *, and Jimeng Sun + * Tsinghua University + IBM TJ Watson Research Center 1 Conformity Conformity is the act of matching attitudes,

Appendix I Teaching outcomes of the degree programme (art. 1.3)

Appendix I Teaching outcomes of the degree programme (art. 1.3) The Master graduate in Computing Science is fully acquainted with the basic terms and techniques used in Computing Science, and is familiar

Artificial Neural Networks in Cardiology - ECG Wave Analysis and Diagnosis Using Backpropagation Neural Networks

Artificial Neural Networks in Cardiology - ECG Wave Analysis and Diagnosis Using Backpropagation Neural Networks 1.Syed Khursheed ul Hasnain C Eng MIEE National University of Sciences & Technology, Pakistan

TRIPODS Workshop: Models & Machine Learning for Causal I. & Decision Making

TRIPODS Workshop: Models & Machine Learning for Causal Inference & Decision Making in Medical Decision Making : and Predictive Accuracy text Stavroula Chrysanthopoulou, PhD Department of Biostatistics

The brain-mind-computer trichotomy: hermeneutic approach

The brain-mind-computer trichotomy: hermeneutic approach Péter Érdi perdi@kzoo.edu Henry R. Luce Professor Center for Complex Systems Studies Kalamazoo College http://people.kzoo.edu/ perdi/ and Institue

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Md Musa Leibniz University of Hannover

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Md Musa Leibniz University of Hannover Agenda Introduction Background Word2Vec algorithm Bias in the data generated from

SCUOLA DI SPECIALIZZAZIONE IN FISICA MEDICA. Sistemi di Elaborazione dell Informazione. Introduzione. Ruggero Donida Labati

SCUOLA DI SPECIALIZZAZIONE IN FISICA MEDICA Sistemi di Elaborazione dell Informazione Introduzione Ruggero Donida Labati Dipartimento di Informatica via Bramante 65, 26013 Crema (CR), Italy http://homes.di.unimi.it/donida

Lesson 6 Learning II Anders Lyhne Christensen, D6.05, INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS

Lesson 6 Learning II Anders Lyhne Christensen, D6.05, anders.christensen@iscte.pt INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS First: Quick Background in Neural Nets Some of earliest work in neural networks

EECS 433 Statistical Pattern Recognition

EECS 433 Statistical Pattern Recognition Ying Wu Electrical Engineering and Computer Science Northwestern University Evanston, IL 60208 http://www.eecs.northwestern.edu/~yingwu 1 / 19 Outline What is Pattern

The Development and Application of Bayesian Networks Used in Data Mining Under Big Data

2017 International Conference on Arts and Design, Education and Social Sciences (ADESS 2017) ISBN: 978-1-60595-511-7 The Development and Application of Bayesian Networks Used in Data Mining Under Big Data

Analysis of acgh data: statistical models and computational challenges

: statistical models and computational challenges Ramón Díaz-Uriarte 2007-02-13 Díaz-Uriarte, R. acgh analysis: models and computation 2007-02-13 1 / 38 Outline 1 Introduction Alternative approaches What

Accuracy and Racial Biases of. Recidivism Prediction Instruments

Accuracy and Racial Biases of Recidivism Prediction Instruments Julia J. Dressel Senior Honors Thesis Advisor: Professor Hany Farid Dartmouth Computer Science Technical Report TR2017-822 May 31, 2017 Abstract

A Decision-Theoretic Approach to Evaluating Posterior Probabilities of Mental Models

A Decision-Theoretic Approach to Evaluating Posterior Probabilities of Mental Models Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella Information Sciences Institute, University of Southern California

Stereotyping and Bias in the Flickr30k Dataset

Stereotyping and Bias in the Flickr30k Dataset Emiel van Miltenburg Vrije Universiteit Amsterdam Multimodal Corpora: Computer vision and language processing (MMC 2016) Background Automatic image description

Bayesian Joint Modelling of Benefit and Risk in Drug Development

Bayesian Joint Modelling of Benefit and Risk in Drug Development EFSPI/PSDM Safety Statistics Meeting Leiden 2017 Disclosure is an employee and shareholder of GSK Data presented is based on human research

Introduction. Patrick Breheny. January 10. The meaning of probability The Bayesian approach Preview of MCMC methods

Introduction Patrick Breheny January 10 Patrick Breheny BST 701: Bayesian Modeling in Biostatistics 1/25 Introductory example: Jane s twins Suppose you have a friend named Jane who is pregnant with twins

Computational Models for Belief Revision, Group Decisions and Cultural Shifts

Computational Models for Belief Revision, Group Decisions and Cultural Shifts Whitman Richards (PI), M. I. T. Computer Science and Artificial Intelligence Laboratory MURI Objective: models that forecast

Lecture 2: Foundations of Concept Learning

Lecture 2: Foundations of Concept Learning Cognitive Systems - Machine Learning Part I: Basic Approaches to Concept Learning Version Space, Candidate Elimination, Inductive Bias last change October 18,

Inferencing in Artificial Intelligence and Computational Linguistics

Inferencing in Artificial Intelligence and Computational Linguistics (http://www.dfki.de/~horacek/infer-ai-cl.html) no classes on 28.5., 18.6., 25.6. 2-3 extra lectures will be scheduled Helmut Horacek