Machine Learning Opportunities and Limitations
|
|
|
- Dayna Hall
- 5 years ago
- Views:
Transcription
1 Machine Learning Opportunities and Limitations Holger H. Hoos LIACS Universiteit Leiden The Netherlands LCDS Conference 2017/11/28
2 The age of computation Clear, precise instructions flawlessly executed 1
3 The age of computation Clear, precise instructions flawlessly executed algorithms = recipes for data processing 1
4 The age of computation Clear, precise instructions flawlessly executed algorithms = recipes for data processing predictable results, behaviour 1
5 The age of computation Clear, precise instructions flawlessly executed algorithms = recipes for data processing predictable results, behaviour performance guarantees 1
6 The age of computation Clear, precise instructions flawlessly executed algorithms = recipes for data processing predictable results, behaviour performance guarantees trusted, effective solutions to complex problems 1
7 The age of advanced computation AI vast amounts of cheap computation 2
8 The age of advanced computation AI vast amounts of cheap computation automatically designed algorithms 2
9 The age of advanced computation AI vast amounts of cheap computation automatically designed algorithms effective but complex, heuristic, black-box methods 2
10 Key idea: explicit programming learning / automatic adaptation to data 3
11 Key idea: explicit programming learning / automatic adaptation to data Success stories: game playing (e.g., Go, poker) 3
12 Key idea: explicit programming learning / automatic adaptation to data Success stories: game playing (e.g., Go, poker) medical diagnosis (lung disease) 3
13 Key idea: explicit programming learning / automatic adaptation to data Success stories: game playing (e.g., Go, poker) medical diagnosis (lung disease) transportation (autonomous driving) 3
14 Key idea: explicit programming learning / automatic adaptation to data Success stories: game playing (e.g., Go, poker) medical diagnosis (lung disease) transportation (autonomous driving) energy (demand prediction and trading) 3
15 The Machine Learning Revolution machine learning (ML) = automatic construction of software that works well on given data 4
16 The Machine Learning Revolution machine learning (ML) = automatic construction of software that works well on given data ideas reach back to 1950s (Alan Turing) 4
17 The Machine Learning Revolution machine learning (ML) = automatic construction of software that works well on given data ideas reach back to 1950s (Alan Turing) based on statistics, mathematical optimisation 4
18 The Machine Learning Revolution machine learning (ML) = automatic construction of software that works well on given data ideas reach back to 1950s (Alan Turing) based on statistics, mathematical optimisation and principled experimentation (heuristic mechanisms) 4
19 The Machine Learning Revolution machine learning (ML) = automatic construction of software that works well on given data ideas reach back to 1950s (Alan Turing) based on statistics, mathematical optimisation and principled experimentation (heuristic mechanisms) key ingredient to artificial intelligence (AI) 4
20 The Machine Learning Revolution machine learning (ML) = automatic construction of software that works well on given data ideas reach back to 1950s (Alan Turing) based on statistics, mathematical optimisation and principled experimentation (heuristic mechanisms) key ingredient to artificial intelligence (AI) but: AI is more than ML 4
21 Supervised vs unsupervised ML unsupervised: discover patterns in data 5
22 Supervised vs unsupervised ML unsupervised: discover patterns in data data mining 5
23 Supervised vs unsupervised ML unsupervised: discover patterns in data data mining (e.g., clustering) 5
24 Supervised vs unsupervised ML unsupervised: discover patterns in data data mining (e.g., clustering) supervised: make predictions based on known training examples 5
25 Supervised vs unsupervised ML unsupervised: discover patterns in data data mining (e.g., clustering) supervised: make predictions based on known training examples statistical modelling 5
26 Supervised vs unsupervised ML unsupervised: discover patterns in data data mining (e.g., clustering) supervised: make predictions based on known training examples statistical modelling Key assumption: training data is representative Key assumption: of application scenario 5
27 Supervised vs unsupervised ML unsupervised: discover patterns in data data mining (e.g., clustering) supervised: make predictions based on known training examples statistical modelling Key assumption: training data is representative Key assumption: of application scenario other types of ML exist (e.g., semi-supervised learning, reinforcement learning) 5
28 Regression Example: predict plant growth for given set Example: of environmental conditions 6
29 Regression Example: predict plant growth for given set Example: of environmental conditions Given: set of training examples Given: = feature values + numerical outputs 6
30 Regression Example: predict plant growth for given set Example: of environmental conditions Given: set of training examples Given: = feature values + numerical outputs Objective: predict output for new feature values 6
31 Classification Example: predict whether someone takes a loan Example: based on demographic + personal financial data 7
32 Classification Example: predict whether someone takes a loan Example: based on demographic + personal financial data Given: set of training examples Given: = feature values + classes 7
33 Classification Example: predict whether someone takes a loan Example: based on demographic + personal financial data Given: set of training examples Given: = feature values + classes Objective: predict class for new feature values 7
34 Classification Example: predict whether someone takes a loan Example: based on demographic + personal financial data Given: set of training examples Given: = feature values + classes Objective: predict class for new feature values Important special case: binary classification Important special case: = 2 classes (e.g., yes/no) 7
35 Example: Binary classification with decision trees [Source: 8
36 Random forests (state-of-the-art method) [Source: blog.citizennet.com] 9
37 Key distinction: Classification procedure (classifier; model ): algorithm used for solving a classification problem e.g., decision tree 10
38 Key distinction: Classification procedure (classifier; model ): algorithm used for solving a classification problem e.g., decision tree Input: feature values Output: class (yes/no) 10
39 Key distinction: Classification procedure (classifier; model ): algorithm used for solving a classification problem e.g., decision tree Input: feature values Output: class (yes/no) Learning procedure: algorithm used for constructing a classifier e.g., C4.5 (well-known decision tree learning algorithm) 10
40 Key distinction: Classification procedure (classifier; model ): algorithm used for solving a classification problem e.g., decision tree Input: feature values Output: class (yes/no) Learning procedure: algorithm used for constructing a classifier e.g., C4.5 (well-known decision tree learning algorithm) Input: set of training data Output: classification procedure (decision tree) 10
41 Evaluation and Bias How to evaluate supervised ML algorithms? Key idea: Assess quality of predictions obtained Key idea: (e.g., from trained binary classifier) 11
42 Evaluation and Bias How to evaluate supervised ML algorithms? Key idea: Assess quality of predictions obtained Key idea: (e.g., from trained binary classifier) Prediction quality of binary classifiers accuracy: expected rate of misclassifications 11
43 Evaluation and Bias How to evaluate supervised ML algorithms? Key idea: Assess quality of predictions obtained Key idea: (e.g., from trained binary classifier) Prediction quality of binary classifiers accuracy: expected rate of misclassifications false positive rate: expected rate of incorrect yes predictions 11
44 Evaluation and Bias How to evaluate supervised ML algorithms? Key idea: Assess quality of predictions obtained Key idea: (e.g., from trained binary classifier) Prediction quality of binary classifiers accuracy: expected rate of misclassifications false positive rate: expected rate of incorrect yes predictions false negative rate: expected rate of incorrect no predictions 11
45 Evaluation and Bias How to evaluate supervised ML algorithms? Key idea: Assess quality of predictions obtained Key idea: (e.g., from trained binary classifier) Prediction quality of binary classifiers accuracy: expected rate of misclassifications false positive rate: expected rate of incorrect yes predictions false negative rate: expected rate of incorrect no predictions trade-off (weighted average; ROC curve) 11
46 Caution: Typically, no single correct evaluation metric evaluation metrics can introduce unfairness / bias 12
47 Caution: Typically, no single correct evaluation metric evaluation metrics can introduce unfairness / bias especially when training sets are unbalanced (many more no than yes cases, prevalence/lack of input feature combinations) 12
48 Caution: Typically, no single correct evaluation metric evaluation metrics can introduce unfairness / bias especially when training sets are unbalanced (many more no than yes cases, prevalence/lack of input feature combinations) use great care when constructing training sets 12
49 Caution: Typically, no single correct evaluation metric evaluation metrics can introduce unfairness / bias especially when training sets are unbalanced (many more no than yes cases, prevalence/lack of input feature combinations) use great care when constructing training sets use multiple evaluation metrics 12
50 Caution: Typically, no single correct evaluation metric evaluation metrics can introduce unfairness / bias especially when training sets are unbalanced (many more no than yes cases, prevalence/lack of input feature combinations) use great care when constructing training sets use multiple evaluation metrics perform detailed evaluations (beyond simple metrics) 12
51 The problem of overfitting good performance on training data may not generalise to previously unseen data overfitting (well-known problem) 13
52 The problem of overfitting good performance on training data may not generalise to previously unseen data overfitting (well-known problem) detect overfitting using validation techniques hold-out validation: evaluate on set of test cases hold-out validation: strictly separate from training set 13
53 The problem of overfitting good performance on training data may not generalise to previously unseen data overfitting (well-known problem) detect overfitting using validation techniques hold-out validation: evaluate on set of test cases hold-out validation: strictly separate from training set cross-validation: like hold-out, but with many different cross-validation: training/test splits 13
54 The problem of overfitting good performance on training data may not generalise to previously unseen data overfitting (well-known problem) detect overfitting using validation techniques hold-out validation: evaluate on set of test cases hold-out validation: strictly separate from training set cross-validation: like hold-out, but with many different cross-validation: training/test splits prevent overfitting using regularisation techniques (= modification / specific setting of ML method used) 13
55 The problem of overfitting good performance on training data may not generalise to previously unseen data overfitting (well-known problem) detect overfitting using validation techniques hold-out validation: evaluate on set of test cases hold-out validation: strictly separate from training set cross-validation: like hold-out, but with many different cross-validation: training/test splits prevent overfitting using regularisation techniques (= modification / specific setting of ML method used) Caution: Overfitting can introduce bias! 13
56 Problematic features certain (input) features can help improve performance, but are inappropriate to use 14
57 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: 14
58 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: race, gender, sexual orientation 14
59 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: race, gender, sexual orientation using problematic features in machine learning can cause (unintentional) discrimination 14
60 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: race, gender, sexual orientation using problematic features in machine learning can cause (unintentional) discrimination Easy solution: do not use problematic features 14
61 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: race, gender, sexual orientation using problematic features in machine learning can cause (unintentional) discrimination Easy solution: do not use problematic features Wrong!! combinations of other, harmless features can yield equivalent information 14
62 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: race, gender, sexual orientation using problematic features in machine learning can cause (unintentional) discrimination Easy solution: do not use problematic features Wrong!! combinations of other, harmless features can yield equivalent information especially problematic for deep learning and other, powerful black-box methods 14
63 Problematic features certain (input) features can help improve performance, but are inappropriate to use examples: race, gender, sexual orientation using problematic features in machine learning can cause (unintentional) discrimination Easy solution: do not use problematic features Wrong!! combinations of other, harmless features can yield equivalent information especially problematic for deep learning and other, powerful black-box methods Better solution: careful, detailed evaluation 14
64 Explainability & Transparency Challenge: How can we trust an ML system? 15
65 Explainability & Transparency Challenge: How can we trust an ML system? carefully evaluate performance; identify strengths and weaknesses (requires detailed evaluation = computational experiments) 15
66 Explainability & Transparency Challenge: How can we trust an ML system? carefully evaluate performance; identify strengths and weaknesses (requires detailed evaluation = computational experiments) understand how it works 15
67 Explainability & Transparency Challenge: How can we trust an ML system? carefully evaluate performance; identify strengths and weaknesses (requires detailed evaluation = computational experiments) understand how it works understand its output 15
68 Key distinction: understanding a classifier (e.g., decision tree) vs understanding the training procedure that produced it 16
69 Key distinction: understanding a classifier (e.g., decision tree) vs understanding the training procedure that produced it Note: to understand a given classifier (and its output), we do not need to understand how it was built 16
70 Key distinction: understanding a classifier (e.g., decision tree) vs understanding the training procedure that produced it Note: to understand a given classifier (and its output), we do not need to understand how it was built understanding of what happens at every step does not mean understanding behaviour of an algorithm 16
71 Key distinction: understanding a classifier (e.g., decision tree) vs understanding the training procedure that produced it Note: to understand a given classifier (and its output), we do not need to understand how it was built understanding of what happens at every step does not mean understanding behaviour of an algorithm some classifiers are easier to understand than others 16
72 Neural networks [Source: 17
73 Deep learning uses neural networks with many layers 18
74 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers 18
75 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers idea + research dates back to 1960s/1970s 18
76 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers idea + research dates back to 1960s/1970s successful real-world applications since the 1980s 18
77 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers idea + research dates back to 1960s/1970s successful real-world applications since the 1980s very popular since
78 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers idea + research dates back to 1960s/1970s successful real-world applications since the 1980s very popular since 2012 impressive results in increasing number of application areas 18
79 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers idea + research dates back to 1960s/1970s successful real-world applications since the 1980s very popular since 2012 impressive results in increasing number of application areas requires large amounts of data, specialised hardware, considerable human expertise + experimentation 18
80 Deep learning uses neural networks with many layers AlphaGo Zero: 84 layers idea + research dates back to 1960s/1970s successful real-world applications since the 1980s very popular since 2012 impressive results in increasing number of application areas requires large amounts of data, specialised hardware, considerable human expertise + experimentation Caution! Deep learning machine learning AI 18
81 Deep neural networks are black-box methods easy to understand function of each neuron in the network; very hard / impossible to understand the behaviour of the network 19
82 Deep neural networks are black-box methods easy to understand function of each neuron in the network; very hard / impossible to understand the behaviour of the network lack of transparency / explainability 19
83 Deep neural networks are black-box methods easy to understand function of each neuron in the network; very hard / impossible to understand the behaviour of the network lack of transparency / explainability Possible remedies: principled, detailed evaluation of behaviour 19
84 Deep neural networks are black-box methods easy to understand function of each neuron in the network; very hard / impossible to understand the behaviour of the network lack of transparency / explainability Possible remedies: principled, detailed evaluation of behaviour use alternate methods with similar performance (e.g., random forests) 19
85 Deep neural networks are black-box methods easy to understand function of each neuron in the network; very hard / impossible to understand the behaviour of the network lack of transparency / explainability Possible remedies: principled, detailed evaluation of behaviour use alternate methods with similar performance (e.g., random forests) trade off performance against explainability 19
86 Deep neural networks are black-box methods easy to understand function of each neuron in the network; very hard / impossible to understand the behaviour of the network lack of transparency / explainability Possible remedies: principled, detailed evaluation of behaviour use alternate methods with similar performance (e.g., random forests) trade off performance against explainability frugal learning (new research direction) 19
87 Automated Machine Learning Machine learning is powerful 20
88 Automated Machine Learning Machine learning is powerful, but successful application is far from trivial. 20
89 Automated Machine Learning Machine learning is powerful, but successful application is far from trivial. Fundamental problem: Which of many available algorithms (models) applicable to given machine learning problem to use, and with which hyper-parameter settings? 20
90 Automated Machine Learning Machine learning is powerful, but successful application is far from trivial. Fundamental problem: Which of many available algorithms (models) applicable to given machine learning problem to use, and with which hyper-parameter settings? Example: WEKA contains 39 classification algorithms, Example: 3 8 feature selection methods 20
91 Automated Machine Learning Machine learning is powerful, but successful application is far from trivial. Fundamental problem: Which of many available algorithms (models) applicable to given machine learning problem to use, and with which hyper-parameter settings? Solution: Automatically select ML methods and hyper-parameter settings 20
92 Automated Machine Learning Machine learning is powerful, but successful application is far from trivial. Fundamental problem: Which of many available algorithms (models) applicable to given machine learning problem to use, and with which hyper-parameter settings? Solution: Automatically select ML methods and hyper-parameter settings Automated machine learning (AutoML) 20
93 AutoML... achieves substantial performance improvements over solutions hand-crafted by human experts 21
94 AutoML... achieves substantial performance improvements over solutions hand-crafted by human experts enables frugal learning (explainable/transparent ML) 21
95 AutoML... achieves substantial performance improvements over solutions hand-crafted by human experts enables frugal learning (explainable/transparent ML) helps non-experts effectively apply ML techniques 21
96 AutoML... achieves substantial performance improvements over solutions hand-crafted by human experts enables frugal learning (explainable/transparent ML) helps non-experts effectively apply ML techniques intense international research focus (academia + industry) 21
97 AutoML... achieves substantial performance improvements over solutions hand-crafted by human experts enables frugal learning (explainable/transparent ML) helps non-experts effectively apply ML techniques intense international research focus (academia + industry) ongoing research focus at LIACS (Leiden Institute of Advanced Computer Science); see ada.liacs.nl/projects, Auto-WEKA. 21
98 Take-Home Message Machine learning can (help to) solve many proplems 22
99 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. 22
100 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. Methods and results strongly depend on quantity + quality of input data. 22
101 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. Methods and results strongly depend on quantity + quality of input data. Challenges: Risk of overfitting training data, hidden bias 22
102 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. Methods and results strongly depend on quantity + quality of input data. Challenges: Risk of overfitting training data, hidden bias Lack of transparency, explainability 22
103 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. Methods and results strongly depend on quantity + quality of input data. Challenges: Risk of overfitting training data, hidden bias Lack of transparency, explainability Human expertise: crucial for successful, responsible use 22
104 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. Methods and results strongly depend on quantity + quality of input data. Challenges: Risk of overfitting training data, hidden bias Lack of transparency, explainability Human expertise: crucial for successful, responsible use Current + future research (far from solved) 22
105 Take-Home Message Machine learning can (help to) solve many proplems... but is no panacea. Methods and results strongly depend on quantity + quality of input data. Challenges: Risk of overfitting training data, hidden bias Lack of transparency, explainability Human expertise: crucial for successful, responsible use Current + future research (far from solved) AI should augment, not replace human expertise! (Likewise for machine learning.) 22
Lecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
CS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
Python Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
Lecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
Artificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
Laboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
CSL465/603 - Machine Learning
CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am
Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks
Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com
ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF
Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
Laboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
Australian Journal of Basic and Applied Sciences
AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean
Learning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
Rule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
Probabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
INPE São José dos Campos
INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA
Data Structures and Algorithms
CS 3114 Data Structures and Algorithms 1 Trinity College Library Univ. of Dublin Instructor and Course Information 2 William D McQuain Email: Office: Office Hours: wmcquain@cs.vt.edu 634 McBryde Hall see
Axiom 2013 Team Description Paper
Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association
Assignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
Human Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
California Professional Standards for Education Leaders (CPSELs)
Standard 1 STANDARD 1: DEVELOPMENT AND IMPLEMENTATION OF A SHARED VISION Education leaders facilitate the development and implementation of a shared vision of learning and growth of all students. Element
Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees
Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,
Lecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
Applications of data mining algorithms to analysis of medical data
Master Thesis Software Engineering Thesis no: MSE-2007:20 August 2007 Applications of data mining algorithms to analysis of medical data Dariusz Matyja School of Engineering Blekinge Institute of Technology
MYCIN. The MYCIN Task
MYCIN Developed at Stanford University in 1972 Regarded as the first true expert system Assists physicians in the treatment of blood infections Many revisions and extensions over the years The MYCIN Task
Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA
Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology
Twitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
Knowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
OCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
Probability estimates in a scenario tree
101 Chapter 11 Probability estimates in a scenario tree An expert is a person who has made all the mistakes that can be made in a very narrow field. Niels Bohr (1885 1962) Scenario trees require many numbers.
Welcome to. ECML/PKDD 2004 Community meeting
Welcome to ECML/PKDD 2004 Community meeting A brief report from the program chairs Jean-Francois Boulicaut, INSA-Lyon, France Floriana Esposito, University of Bari, Italy Fosca Giannotti, ISTI-CNR, Pisa,
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad
Softprop: Softmax Neural Network Backpropagation Learning
Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science
Universidade do Minho Escola de Engenharia
Universidade do Minho Escola de Engenharia Universidade do Minho Escola de Engenharia Dissertação de Mestrado Knowledge Discovery is the nontrivial extraction of implicit, previously unknown, and potentially
STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.
STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he
The Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
Seminar - Organic Computing
Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts
A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and
A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and Planning Overview Motivation for Analyses Analyses and
Generative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
A Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
Machine Learning from Garden Path Sentences: The Application of Computational Linguistics
Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,
Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study
Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information
Rule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
Issues in the Mining of Heart Failure Datasets
International Journal of Automation and Computing 11(2), April 2014, 162-179 DOI: 10.1007/s11633-014-0778-5 Issues in the Mining of Heart Failure Datasets Nongnuch Poolsawad 1 Lisa Moore 1 Chandrasekhar
Word Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning Hendrik Blockeel and Joaquin Vanschoren Computer Science Dept., K.U.Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS
COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS L. Descalço 1, Paula Carvalho 1, J.P. Cruz 1, Paula Oliveira 1, Dina Seabra 2 1 Departamento de Matemática, Universidade de Aveiro (PORTUGAL)
Computerized Adaptive Psychological Testing A Personalisation Perspective
Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES
Using focal point learning to improve human machine tacit coordination
DOI 10.1007/s10458-010-9126-5 Using focal point learning to improve human machine tacit coordination InonZuckerman SaritKraus Jeffrey S. Rosenschein The Author(s) 2010 Abstract We consider an automated
Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach To cite this
Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley
Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling
Aalya School. Parent Survey Results
Aalya School Parent Survey Results 2016-2017 Parent Survey Results Academic Year 2016/2017 September 2017 Research Office The Research Office conducts surveys to gather qualitative and quantitative data
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,
Evolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,
Abu Dhabi Indian. Parent Survey Results
Abu Dhabi Indian Parent Survey Results 2016-2017 Parent Survey Results Academic Year 2016/2017 September 2017 Research Office The Research Office conducts surveys to gather qualitative and quantitative
Model Ensemble for Click Prediction in Bing Search Ads
Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com
Abu Dhabi Grammar School - Canada
Abu Dhabi Grammar School - Canada Parent Survey Results 2016-2017 Parent Survey Results Academic Year 2016/2017 September 2017 Research Office The Research Office conducts surveys to gather qualitative
Switchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
Active Learning. Yingyu Liang Computer Sciences 760 Fall
Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,
Business Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence
Business Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence COURSE DESCRIPTION This course presents computing tools and concepts for all stages
For Jury Evaluation. The Road to Enlightenment: Generating Insight and Predicting Consumer Actions in Digital Markets
FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO The Road to Enlightenment: Generating Insight and Predicting Consumer Actions in Digital Markets Jorge Moreira da Silva For Jury Evaluation Mestrado Integrado
Software Maintenance
1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories
CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University
CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9
Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science
Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science Gilberto de Paiva Sao Paulo Brazil (May 2011) gilbertodpaiva@gmail.com Abstract. Despite the prevalence of the
Chinese Language Parsing with Maximum-Entropy-Inspired Parser
Chinese Language Parsing with Maximum-Entropy-Inspired Parser Heng Lian Brown University Abstract The Chinese language has many special characteristics that make parsing difficult. The performance of state-of-the-art
A study of speaker adaptation for DNN-based speech synthesis
A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,
Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE
EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
TD(λ) and Q-Learning Based Ludo Players
TD(λ) and Q-Learning Based Ludo Players Majed Alhajry, Faisal Alvi, Member, IEEE and Moataz Ahmed Abstract Reinforcement learning is a popular machine learning technique whose inherent self-learning ability
Learning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
Exploration. CS : Deep Reinforcement Learning Sergey Levine
Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?
Knowledge based expert systems D H A N A N J A Y K A L B A N D E
Knowledge based expert systems D H A N A N J A Y K A L B A N D E What is a knowledge based system? A Knowledge Based System or a KBS is a computer program that uses artificial intelligence to solve problems
Test Effort Estimation Using Neural Network
J. Software Engineering & Applications, 2010, 3: 331-340 doi:10.4236/jsea.2010.34038 Published Online April 2010 (http://www.scirp.org/journal/jsea) 331 Chintala Abhishek*, Veginati Pavan Kumar, Harish
Using computational modeling in language acquisition research
Chapter 8 Using computational modeling in language acquisition research Lisa Pearl 1. Introduction Language acquisition research is often concerned with questions of what, when, and how what children know,
Calibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
Linking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University
IT Students Workshop within Strategic Partnership of Leibniz University and Peter the Great St. Petersburg Polytechnic University 06.11.16 13.11.16 Hannover Our group from Peter the Great St. Petersburg
ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 98 (2016 ) 368 373 The 6th International Conference on Current and Future Trends of Information and Communication Technologies
Introduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
Reducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application
International Journal of Medical Science and Clinical Inventions 4(3): 2768-2773, 2017 DOI:10.18535/ijmsci/ v4i3.8 ICV 2015: 52.82 e-issn: 2348-991X, p-issn: 2454-9576 2017, IJMSCI Research Article Comparison
arxiv: v2 [cs.cv] 30 Mar 2017
![arxiv: v2 [cs.cv] 30 Mar 2017](/thumbs/71/66193079.jpg "arxiv: v2 [cs.cv] 30 Mar 2017")
Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and
Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems
Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker Jeanne C. Fromer Shrikanth Narayanan walker@research.att.com jeannie@ai.mit.edu shri@research.att.com
Speech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC
On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these
arxiv: v1 [cs.cl] 2 Apr 2017
![arxiv: v1 [cs.cl] 2 Apr 2017](/thumbs/71/66163758.jpg "arxiv: v1 [cs.cl] 2 Apr 2017")
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,
Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1
Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial
The Internet as a Normative Corpus: Grammar Checking with a Search Engine
The Internet as a Normative Corpus: Grammar Checking with a Search Engine Jonas Sjöbergh KTH Nada SE-100 44 Stockholm, Sweden jsh@nada.kth.se Abstract In this paper some methods using the Internet as a
AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS
AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS R.Barco 1, R.Guerrero 2, G.Hylander 2, L.Nielsen 3, M.Partanen 2, S.Patel 4 1 Dpt. Ingeniería de Comunicaciones. Universidad de Málaga.
Web as Corpus. Corpus Linguistics. Web as Corpus 1 / 1. Corpus Linguistics. Web as Corpus. web.pl 3 / 1. Sketch Engine. Corpus Linguistics
(L615) Markus Dickinson Department of Linguistics, Indiana University Spring 2013 The web provides new opportunities for gathering data Viable source of disposable corpora, built ad hoc for specific purposes
Data Fusion Through Statistical Matching
A research and education initiative at the MIT Sloan School of Management Data Fusion Through Statistical Matching Paper 185 Peter Van Der Puttan Joost N. Kok Amar Gupta January 2002 For more information,
Knowledge-Based - Systems
Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University