The traditional approach to classification testing is extremely inefficient and often difficult to implement in applied settings. Typically, examinees are rank ordered either through Item Response Theory or Classical Test Theory, and then scores are compared to a difficult-to-define cut score.
This webinar will introduce the use of decision theory which basically asks: “Does this response pattern look like the response pattern of a master or a non-master?” This simpler model has major advantages over IRT and CTT:
1. Only a small sample of clear masters and a small sample of clear non-masters are needed to calibrate questions.
2. There are no assumptions for unidimensionality, and normal distribution or requirement for monotonically increasing probabilities of correct responses.
This model is attractive and a natural for end-of-unit examinations, adaptive testing, and as the routing mechanism for intelligent tutoring systems.
This webinar will explain the model, identify current applications, and introduce free tools for generating, calibrating and scoring data.
Sara Hooker & Sean McPherson, Delta Analytics, at MLconf Seattle 2017MLconf
Abstract summary
Data Science for Good: Stopping Illegal Deforestation Using Deep Learning:
Interested in using your data skills to give back? Delta Analytics is a Bay Area non-profit that provides free data science consulting to grant recipients all over the world. Rainforest Connection, a Delta grant recipient, worked with Delta fellows to detect illegal deforestation by applying deep learning to audio streamed from rainforests in Peru, Ecuador and Brazil. We will share insights from our work with Rainforest Connection, discuss our fellowship and partnership process, and suggest some best practices for skill based volunteering.
Aaron Roth, Associate Professor, University of Pennsylvania, at MLconf NYC 2017MLconf
Aaron Roth is an Associate Professor of Computer and Information Sciences at the University of Pennsylvania, affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. Previously, he received his PhD from Carnegie Mellon University and spent a year as a postdoctoral researcher at Microsoft Research New England. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and a Yahoo! ACE award. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory and mechanism design, learning theory, and the intersections of these topics. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.”
Abstract Summary:
Differential Privacy and Machine Learning:
In this talk, we will give a friendly introduction to Differential Privacy, a rigorous methodology for analyzing data subject to provable privacy guarantees, that has recently been widely deployed in several settings. The talk will specifically focus on the relationship between differential privacy and machine learning, which is surprisingly rich. This includes both the ability to do machine learning subject to differential privacy, and tools arising from differential privacy that can be used to make learning more reliable and robust (even when privacy is not a concern).
Temporal Learning and Sequence Modeling for a Job Recommender SystemAnoop Kumar
Our approach to the job recommendation task for the
Recsys Challenge 2016. The main contribution of our work is
to combine temporal learning with sequence modeling to capture complex, temporal-related user-item activity patterns to improve job recommendation.
Hanie Sedghi, Research Scientist at Allen Institute for Artificial Intelligen...MLconf
Hanie Sedghi is a Research Scientist at Allen Institute for Artificial Intelligence (AI2). Her research interests include large-scale machine learning, high-dimensional statistics and probabilistic models. More recently, she has been working on inference and learning in latent variable models. She has received her Ph.D. from University of Southern California with a minor in Mathematics in 2015. She was also a visiting researcher at University of California, Irvine working with professor Anandkumar during her Ph.D. She received her B.Sc. and M.Sc. degree from Sharif University of Technology, Tehran, Iran.
Abstract summary
Beating Perils of Non-convexity:Guaranteed Training of Neural Networks using Tensor Methods:
Neural networks have revolutionized performance across multiple domains such as computer vision and speech recognition. However, training a neural network is a highly non-convex problem and the conventional stochastic gradient descent can get stuck in spurious local optima. We propose a computationally efficient method for training neural networks that also has guaranteed risk bounds. It is based on tensor decomposition which is guaranteed to converge to the globally optimal solution under mild conditions. We explain how this framework can be leveraged to train feedforward and recurrent neural networks.
Erik Bernhardsson is the CTO at Better, a small startup in NYC working with mortgages. Before Better, he spent five years at Spotify managing teams working with machine learning and data analytics, in particular music recommendations.
Abstract Summary:
Nearest Neighbor Methods And Vector Models: Vector models are being used in a lot of different fields: natural language processing, recommender systems, computer vision, and other things. They are fast and convenient and are often state of the art in terms of accuracy. One of the challenges with vector models is that as the number of dimensions increase, finding similar items gets challenging. Erik developed a library called “Annoy” that uses a forest of random tree to do fast approximate nearest neighbor queries in high dimensional spaces. We will cover some specific applications of vector models with and how Annoy works.
Sara Hooker & Sean McPherson, Delta Analytics, at MLconf Seattle 2017MLconf
Abstract summary
Data Science for Good: Stopping Illegal Deforestation Using Deep Learning:
Interested in using your data skills to give back? Delta Analytics is a Bay Area non-profit that provides free data science consulting to grant recipients all over the world. Rainforest Connection, a Delta grant recipient, worked with Delta fellows to detect illegal deforestation by applying deep learning to audio streamed from rainforests in Peru, Ecuador and Brazil. We will share insights from our work with Rainforest Connection, discuss our fellowship and partnership process, and suggest some best practices for skill based volunteering.
Aaron Roth, Associate Professor, University of Pennsylvania, at MLconf NYC 2017MLconf
Aaron Roth is an Associate Professor of Computer and Information Sciences at the University of Pennsylvania, affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. Previously, he received his PhD from Carnegie Mellon University and spent a year as a postdoctoral researcher at Microsoft Research New England. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and a Yahoo! ACE award. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory and mechanism design, learning theory, and the intersections of these topics. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.”
Abstract Summary:
Differential Privacy and Machine Learning:
In this talk, we will give a friendly introduction to Differential Privacy, a rigorous methodology for analyzing data subject to provable privacy guarantees, that has recently been widely deployed in several settings. The talk will specifically focus on the relationship between differential privacy and machine learning, which is surprisingly rich. This includes both the ability to do machine learning subject to differential privacy, and tools arising from differential privacy that can be used to make learning more reliable and robust (even when privacy is not a concern).
Temporal Learning and Sequence Modeling for a Job Recommender SystemAnoop Kumar
Our approach to the job recommendation task for the
Recsys Challenge 2016. The main contribution of our work is
to combine temporal learning with sequence modeling to capture complex, temporal-related user-item activity patterns to improve job recommendation.
Hanie Sedghi, Research Scientist at Allen Institute for Artificial Intelligen...MLconf
Hanie Sedghi is a Research Scientist at Allen Institute for Artificial Intelligence (AI2). Her research interests include large-scale machine learning, high-dimensional statistics and probabilistic models. More recently, she has been working on inference and learning in latent variable models. She has received her Ph.D. from University of Southern California with a minor in Mathematics in 2015. She was also a visiting researcher at University of California, Irvine working with professor Anandkumar during her Ph.D. She received her B.Sc. and M.Sc. degree from Sharif University of Technology, Tehran, Iran.
Abstract summary
Beating Perils of Non-convexity:Guaranteed Training of Neural Networks using Tensor Methods:
Neural networks have revolutionized performance across multiple domains such as computer vision and speech recognition. However, training a neural network is a highly non-convex problem and the conventional stochastic gradient descent can get stuck in spurious local optima. We propose a computationally efficient method for training neural networks that also has guaranteed risk bounds. It is based on tensor decomposition which is guaranteed to converge to the globally optimal solution under mild conditions. We explain how this framework can be leveraged to train feedforward and recurrent neural networks.
Erik Bernhardsson is the CTO at Better, a small startup in NYC working with mortgages. Before Better, he spent five years at Spotify managing teams working with machine learning and data analytics, in particular music recommendations.
Abstract Summary:
Nearest Neighbor Methods And Vector Models: Vector models are being used in a lot of different fields: natural language processing, recommender systems, computer vision, and other things. They are fast and convenient and are often state of the art in terms of accuracy. One of the challenges with vector models is that as the number of dimensions increase, finding similar items gets challenging. Erik developed a library called “Annoy” that uses a forest of random tree to do fast approximate nearest neighbor queries in high dimensional spaces. We will cover some specific applications of vector models with and how Annoy works.
This report includes information about:
1. Pre-Processing Variables
a. Treating Missing Values
b. Treating correlated variables
2. Selection of Variables using random forest weights
3. Building model to predict donors and amount expected to be donated.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
Mathematics online: some common algorithmsMark Moriarty
Brief overview of some basic algorithms used online and across data-mining, and a word on where to learn them. Prepared specially for UCC Boole Prize 2012.
A review of the basic ideas and concepts in reinforcement learning, including discussion of Q-Learning and Sarsa methods. Includes a survey of modern RL methods, including Dyna-Q, DQN, REINFORCE, and AC2, and how they relate.
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distrib...MLAI2
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
Caveon Webinar Series: Improving Testing with Key Strength Analysis Caveon Test Security
Improving Testing with Key Strength Analysis
Have you ever wondered whether some distractors were just a little too close to being a right answer? Have you wished you had a way to decide whether an item's answer choice did not meet your standard? What about those items which were published with the wrong answer key?
If you have ever asked yourself these questions, be sure to watch our webinar, presented as part of the Caveon Webinar Series on September 18, 2013. You will learn a new evaluation method that will help you feel confident about your key strength.
The webinar will discuss the underlying concepts, the theory, and applications for the method Caveon has been using since 2011. The method uses classical item statistics, so it can be used for all assessments that can be analyzed using p-values and point-biserial correlations. As such, we believe it to be a valuable enhancement to other commonly-used item analyses.
Testing industry veterans John Fremer and Steve Addicott of Caveon are joined by Lou Woodruff, past president of the National College Testing Association to share their "lessons learned" from several of this summer's biggest testing conferences. For more information, please go to www.caveon.com
Caveon Webinar Series - Ten Test Security Lessons Learned at ATP 2014 march 2014Caveon Test Security
This year's Association of Test Publishers (ATP) Innovations in Testing Conference focused more than ever on test security, and the Caveon team was there. We share with you not only the concepts which we presented, but also new things we learned at the conference.
Caveon leaders John Fremer and Steve Addicott summarize the test security ideas and strategies from ATP and the things you can do to protect your high-stakes tests.
Caveon Webinar Series: Lessons Learned from EATP and CSDPTF November 2013Caveon Test Security
Presented by Dr. John Fremer, Dennis Maynes and Steve Addicott, Caveon Test Security
Two important industry conferences have been held in the last couple of months, the European Association of Test Publishers (E-ATP) Conference and the Conference on Statistical Detection of Potential Test Fraud (CSDPTF). Caveon was at both of these events and wants to share some important information with you.
Join Dr. John Fremer, President of Caveon Consulting Services, Steve Addicott, Vice President, Sales and Marketing, and Dennis Maynes, Chief Scientist, Caveon Data Forensics, who attended both conferences and presented sessions. They will explore key takeaways and lessons learned on security. Stay updated on the latest and greatest industry security trends.
Caveon Webinar Series - Will the Real Cloned Item Please Stand Up? finalCaveon Test Security
Join us for this month's webinar on the ins and outs of developing item clones. While many of us are aware of the benefits cloning can provide, such as expanding an item bank, lengthening the shelf life of an exam, or deterring and detecting cheating, questions remain regarding the best practices for implementation. Secure exam development experts will address the question, "How do we know, during development, when an item has been sufficiently altered, making it a "real clone" and not just an "imitator" of a clone?" The answer isn't as clear cut as it would seem.
Additional topics will include:
• General information on cloning
• Lessons learned from the field
• Creative ideas for streamlining cloning processes
This webinar will help assessment and program managers be better positioned to put on their cloning lab coats and reap the rewards of this best practice in test security.
Caveon Webinar Series - Lessons Learned at the 2015 National Conference on S...Caveon Test Security
The National Conference on Student Assessment (NCSA) was held last month in San Diego, and Caveon was there. This month's webinar will focus on lessons learned at the conference regarding test security, and what's happening in the state assessment arena in terms of test security right now.
Caveon's Steve Addicott and Jamie Mulkey will be joined by special guest Walt Drane, State Assessment Director, Mississippi Department of Education. The panelists will summarize the test security trends and strategies that they drew from the conference, and also share key points from sessions they presented.
This report includes information about:
1. Pre-Processing Variables
a. Treating Missing Values
b. Treating correlated variables
2. Selection of Variables using random forest weights
3. Building model to predict donors and amount expected to be donated.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
Mathematics online: some common algorithmsMark Moriarty
Brief overview of some basic algorithms used online and across data-mining, and a word on where to learn them. Prepared specially for UCC Boole Prize 2012.
A review of the basic ideas and concepts in reinforcement learning, including discussion of Q-Learning and Sarsa methods. Includes a survey of modern RL methods, including Dyna-Q, DQN, REINFORCE, and AC2, and how they relate.
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distrib...MLAI2
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
Caveon Webinar Series: Improving Testing with Key Strength Analysis Caveon Test Security
Improving Testing with Key Strength Analysis
Have you ever wondered whether some distractors were just a little too close to being a right answer? Have you wished you had a way to decide whether an item's answer choice did not meet your standard? What about those items which were published with the wrong answer key?
If you have ever asked yourself these questions, be sure to watch our webinar, presented as part of the Caveon Webinar Series on September 18, 2013. You will learn a new evaluation method that will help you feel confident about your key strength.
The webinar will discuss the underlying concepts, the theory, and applications for the method Caveon has been using since 2011. The method uses classical item statistics, so it can be used for all assessments that can be analyzed using p-values and point-biserial correlations. As such, we believe it to be a valuable enhancement to other commonly-used item analyses.
Testing industry veterans John Fremer and Steve Addicott of Caveon are joined by Lou Woodruff, past president of the National College Testing Association to share their "lessons learned" from several of this summer's biggest testing conferences. For more information, please go to www.caveon.com
Caveon Webinar Series - Ten Test Security Lessons Learned at ATP 2014 march 2014Caveon Test Security
This year's Association of Test Publishers (ATP) Innovations in Testing Conference focused more than ever on test security, and the Caveon team was there. We share with you not only the concepts which we presented, but also new things we learned at the conference.
Caveon leaders John Fremer and Steve Addicott summarize the test security ideas and strategies from ATP and the things you can do to protect your high-stakes tests.
Caveon Webinar Series: Lessons Learned from EATP and CSDPTF November 2013Caveon Test Security
Presented by Dr. John Fremer, Dennis Maynes and Steve Addicott, Caveon Test Security
Two important industry conferences have been held in the last couple of months, the European Association of Test Publishers (E-ATP) Conference and the Conference on Statistical Detection of Potential Test Fraud (CSDPTF). Caveon was at both of these events and wants to share some important information with you.
Join Dr. John Fremer, President of Caveon Consulting Services, Steve Addicott, Vice President, Sales and Marketing, and Dennis Maynes, Chief Scientist, Caveon Data Forensics, who attended both conferences and presented sessions. They will explore key takeaways and lessons learned on security. Stay updated on the latest and greatest industry security trends.
Caveon Webinar Series - Will the Real Cloned Item Please Stand Up? finalCaveon Test Security
Join us for this month's webinar on the ins and outs of developing item clones. While many of us are aware of the benefits cloning can provide, such as expanding an item bank, lengthening the shelf life of an exam, or deterring and detecting cheating, questions remain regarding the best practices for implementation. Secure exam development experts will address the question, "How do we know, during development, when an item has been sufficiently altered, making it a "real clone" and not just an "imitator" of a clone?" The answer isn't as clear cut as it would seem.
Additional topics will include:
• General information on cloning
• Lessons learned from the field
• Creative ideas for streamlining cloning processes
This webinar will help assessment and program managers be better positioned to put on their cloning lab coats and reap the rewards of this best practice in test security.
Caveon Webinar Series - Lessons Learned at the 2015 National Conference on S...Caveon Test Security
The National Conference on Student Assessment (NCSA) was held last month in San Diego, and Caveon was there. This month's webinar will focus on lessons learned at the conference regarding test security, and what's happening in the state assessment arena in terms of test security right now.
Caveon's Steve Addicott and Jamie Mulkey will be joined by special guest Walt Drane, State Assessment Director, Mississippi Department of Education. The panelists will summarize the test security trends and strategies that they drew from the conference, and also share key points from sessions they presented.
Caveon Webinar Series - Lessons Learned at the European Association of Test...Caveon Test Security
Join us for some groundbreaking exam security developments
Several members of the Caveon team just returned from the first annual Conference on Test Security in Iowa City, where a gathering of industry experts presented information and key developments that are essential in keeping the road to secure exams soundly paved.
Caveon's Steve Addicott and John Fremer will share messages from the sessions they presented, and what they learned from others during the conference. Steve and John are industry veterans, and have decades of experience in test security. They know what security lessons are most important to testing programs today.
They will be joined by industry rock star, Rachel Schoenig, Assistant Vice President and Head of Test Security at ACT, who presented several sessions on the agenda, including one focused on measuring the effectiveness of your test security program.
In addition, Steve and John attended and presented at the European Association of Test Publisher's (EATP) annual conference in late September, and will share a few lessons learned from the international gathering as well.
This could be the most important webinar you attend this year.
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Gradient Boosted Regression Trees in scikit-learnDataRobot
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014.
Abstract:
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price.
I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...sugiuralab
Wearable Accelerometer Optimal Positions for Human Motion Recognition. The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2020), March 10-11, 2020
Machine Learning Essentials Demystified part2 | Big Data DemystifiedOmid Vahdaty
achine Learning Essentials Abstract:
Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about?
In this session we will talk about what ML actually is and in which cases it is useful.
We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python.
The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
Caveon Webinar Series - A Guide to Online Protection Strategies - March 28, ...Caveon Test Security
Join Executive Web Patrol Managers, Cary Straw and Jen Baldwin, as we explore the systems, methods and steps you need to successfully protect and extend the life of your high stakes certification, licensure, and state assessment exams from online threats.
Some of the questions we will answer include:
• Which processes should I implement to decrease the chance of my content appearing online?
• Where are the best places to use online security resources?
• Where do I look next if I found a threat, and where are the threats likely to spread?
• What are proactive steps I can take to protect my exams online?
• Who should be in my protection hierarchy?
• Am I "safe" after I've found a threat, and have had it removed?
Caveon Webinar Series - Five Things You Can Do Now to Protect Your Assessment...Caveon Test Security
Test season is approaching quickly! Maintaining the security and validity of assessment results is critical to support federal accountability and peer review requirements.
Kick off testing season with this year's first Caveon Webinar, "Five Things You Can Do Right Now to Protect Your Assessment Programs."
This webinar will focus on:
• Test security threats & risk analysis
• Creating test security policies and procedures
• Planning and implementing on-site monitoring
• Reviewing anomalous test results
• Managing incident reports
Join the webinar to learn more, and you'll be off to a strong start in protecting your tests, your results, and your reputation.
If you missed the first three sessions, you can still view them. And, if you can't attend on January 17, go ahead and register anyway and we will send you the recording and slides after the session.
The Do's and Dont's of Administering High Stakes Tests in Schools Final 121217Caveon Test Security
There is a great deal of advice available about giving high stakes tests securely in school settings. States run annual training sessions and provide test administration manuals. Major vendors serving schools provide training and guidelines of varying types. Sometimes the different sources disagree and the emphases vary by the nature of the helping agency. What is a test administrator to do?
This webinar focuses on administering tests in schools and identifies ten "best practices" that apply to all high stakes testing. The content is drawn from careful analyses of current testing practices by states, districts, and testing vendors.
To be an effective test administrator, you will need to read the background materials about each testing program and attend any training that is provided. If you also follow the guidelines presented in this webinar, you will be in a very good position to promote fairness and validity in each of the programs for which you share responsibility.
In this webinar, you will learn:
* Ten Best Practices that apply to all high stakes testing
* What is required to be an effective test administrator
* How to promote fairness and validity in your testing programs
Sponsored by the National Association of Assessment Directors and Caveon Consulting Services, Caveon Test Security
Caveon Webinar Series - The Art of Test Security - Know Thy Enemy - November ...Caveon Test Security
As Sun Tsu famously said... "If you know your enemy as you know yourself, you need not fear 100 battles." On the battlefield of security -- whether home security, airport security, or test security - the first step to success is knowing the threats.
Are you worried about tests being stolen and shared online? Or test takers cheating by being coached by an expert? If so, the steps to successfully protecting your test and triumphing over these fears include:
• conducting a risk assessment
• determining (and ranking) which threats pose the greatest risk
• strategizing how to render those threats impotent
• determining the right combination of prevention, detection and deterrence tactics for your program
This webinar will teach you to conquer the steps in this test security process. Join Caveon CEO David Foster to learn how to analyze and rank the threats that are specific to your program. You will also discover the three solutions necessary to counter any and all of these threats.
Caveon Webinar Series - Four Steps to Effective Investigations in School Dis...Caveon Test Security
Now that spring test administrations are almost over, K-12 districts and schools can breathe a sigh of relief. Weeks of vigilance have paid off with a smooth, incident-free test administration. Not your district? You’re not alone. No matter the extent of planning, training, and oversight, there are always unforeseen events that result in testing irregularities. Most will be straightforward and covered by standard policies and procedures. But some incidents may set off your internal alarms. By themselves, these reports are only single data points and need to be explored to determine the larger context and what really happened. This webinar will provide information on:
How to develop a plan for responding to test irregularity reports and;
How to carry out investigations if additional information is needed.
The session is free, and will only last 30 minutes. Space is limited, so register today! We look forward to seeing you on May 18th!
If you missed the first two sessions, you can still view them. And, if you can't attend on May 18, go ahead and register anyway and we will automatically send you the recording and slides after the session.
Caveon Webinar Series - On-site Monitoring in Districts 0317Caveon Test Security
Are you sure that school leaders and educators are following your state and local assessment policies and procedures during the administration of assessments?
On-site monitoring of assessment administrations at schools and in classrooms is an effective quality assurance measure that:
• ensures compliance with standardized policies and procedures
• helps identify the greatest areas of vulnerability in your assessment administration processes
• creates opportunities to improve training, and
• clarifies messaging about assessments for school leaders and educators.
Finally, LEA-sponsored monitoring demonstrates a strong commitment to the integrity of assessments and the important decisions made based upon assessment results.
By attending this webinar, you will gain exposure to:
1) the goals and purposes of monitoring,
2) best-practice monitoring activities during assessment administrations,
3) evaluating data from monitoring reports,
4) potential outcomes from monitoring and
5) first steps in implementing a monitoring program.
Caveon Test Security, the industry leader in providing security solutions for protecting high-stakes, K-12 assessments, is pleased to announce the first webinar in a series of 3, focused on test security challenges faced specifically by districts.
Session #1: Avoiding A School District Test Cheating Scandal:
A Tale of Two Cities
January 25, 2017, 12:00 p.m. ET
As a number of U.S. school districts have learned, mishandling of cheating incidents on tests, particularly state assessments, can have very negative and pervasive effects. This webinar reviews two examples of actual test cheating situations in school districts, contrasts how they were handled, and lays out practical and "battle-tested" strategies for avoiding and, if necessary, coping with test cheating events. Having a strong security plan and acting wisely and decisively when you see signs of trouble can be a very productive approach. This webinar will give you tools to manage a test cheating incident if you have a suspected or confirmed report of cheating.
Caveon Webinar Series - Discrete Option Multiple Choice: A Revolution in Te...Caveon Test Security
High-stakes testing faces major changes due to the use of computers and other technology in test administration. Some such changes include new test designs (such as computerized adaptive testing), proctoring tests online, and even administering tests on tablets and smartphones to improve test taker convenience. One of the most important changes is innovative new item types that better measure important skills. The Discrete Option Multiple Choice item type, or DOMC, is one of these ground-breaking new item types.
The DOMC item has the potential to revolutionize testing. It brings significant benefits in security, quality of measurement, fairness, test development, and test administration.
Caveon Webinar Series - Test Cheaters Say the Darnedest Things! - 072016Caveon Test Security
You won't believe what's actually happened in the world of testing!
What goes on in the mind of a would-be test cheater? While cheating is a serious offense, some of test takers go to great (and sometimes comical) lengths to try gaining an unfair advantage to achieve a successful testing outcome.
Join us as we look at some of the most memorable proctor/test taker cheating encounters. Our special guest, Jarret Dyer, of the College of DuPage Testing Center, has created a compilation of test proctor stories from testing centers around the United States and across the globe. Jarret will share his 'best of' stories, while Caveon's John Fremer will discuss the consequences of not following the right test security processes and procedures. You don't want to miss this fun, yet informative session! To listen to the recording that goes along with these slides, go to https://youtu.be/r-CCaDf7NEk
Caveon Webinar Series - The Test Security Framework- Why Different Tests Nee...Caveon Test Security
The need for global workforce skills credentials continues to grow. At the same time, the global workforce is shrinking. It is imperative that skill recognition be accurate and the level of test security be appropriate for the skills being assessed. The Security subcommittee of the new Workforce Skills Credentialing division of ATP created a new test security framework that will provide guidance to testing organizations when selecting the level of security needed for their assessments.
Join our guest presenters, Rachel Schoenig and Jennifer Geraets of ACT, as they discuss the challenge of identifying global workforce skills and how this new test security framework will help to align the expectations of those involved with workforce credentialing (e.g., test publishers, examinees, and employers). Rachel and Jennifer will also provide a call to action, requesting your comments on this new framework.
Caveon Webinar Series - Conducting Test Security Investigations in School Di...Caveon Test Security
In the coming weeks, schools all over the country will be administering standardized exams to millions of students. And inevitably, test security incidents will arise, many of which may directly impact test score validity. Is your team prepared to answer the following tough questions?
• What will you do if you find yourself in a position of having to respond to an incident or breach in your state or district?
• What process will you follow?
• What is your incident escalation plan?
• How will you communicate with internal and external stakeholders?
• Most importantly, how will you discover the truth of what did or did not occur, and its impact on test scores?
Join Caveon’s test security experts for an important, hour-long webinar to help you understand the steps to take when challenging situations arise. We will share:
• Recent experiences other districts have had with possible cheating, and what they have done to resolve their concerns
• Information and tools for you to arm yourself before an issue arises, and to help you be better equipped to deal effectively and efficiently
• Essential tips you need to know when invoking a Security Incident Response Plan, and further conducting a security investigation
Caveon Webinar Series - Creating Your Test Security Game Plan - March 2016Caveon Test Security
History has shown that as stakes rise for testing programs, so do threats to the program's test result validity. There are stories in the media almost daily about high-stakes programs suffering at the hands of those intent on obtaining the content for disingenuous purposes. Having a game plan in place before a threat or validity issue occurs is vital. This month's webinar will focus on key steps your organization can take to maximize your protection from test fraud, and stay one step ahead of the game.
Caveon Webinar Series - Mastering the US DOE Test Security Requirements Janua...Caveon Test Security
The U.S. Department of Education recently issued the Peer Review of State Assessment Systems, which includes a required "Critical Element" on Test Security. To fulfill this requirement, States must submit documentation of policies and procedures in four categories of test security: prevention, detection, remediation, and investigation.
It is up to each State to determine which steps to implement and what evidence to submit to prove they have met each of these requirements. Evidence could, and should, include a myriad of test security measures ranging from Security Handbooks and annual proctor training, to data forensics and web monitoring procedures (and everything in between).
Caveon can help guide you through this complicated process. In the upcoming session, our test security experts will unpack the requirements of this section of the Peer Review process. The goal is to help you form a road map moving forward, provide information on the best practices for protecting your assessments, and outline resources to streamline the process.
Caveon Webinar Series - Learning and Teaching Best Practices in Test Security...Caveon Test Security
Test security has been emerging as a cohesive discipline for the past ten years. There are no college courses that teach test security. And, even if there were, many practitioners don't have time to take those classes. How do you stay abreast of current developments? How do you train your staff in latest best practices if you don't know about them? Are there resources out there, and how do you find them?
In this webinar, Caveon will host several special guest practitioners from various industries. These test security veterans have had to answer these very questions. They will address how continuing education will help you improve test security in your organization.
Caveon Webinar Series - Weathering the Perfect Test Security Storm May 2015Caveon Test Security
In recent years, test security issues have received greater attention, almost to the point of distraction, by school system administrators. Motivations to cheat on state assessments appear to be higher than ever. The number of test security violations, the severity of breaches, and risks to state assessments have been increasing. Members of the PARCC and SBAC consortia are using the same tests, increasing the likelihood that actual content of state assessments will be illicitly distributed on the internet. Unless action is taken soon, we may experience the perfect test security storm in state assessments. This storm is likely to result in more educators being forced to contend with security issues, more revelations of test security breaches, and more emergency funding requests to deal with the aftermath. Presenters of this webinar will explain why this has happened and what state departments of education and school districts can do to handle and mitigate test security breaches.
Important, timely topics to be covered in the presentation are:
* Best practices that can be used to prevent, detect, and respond to breaches.
* Recommendations for formalizing processes and adopting a quality improvement approach to test security.
* Suggestions for how to measure, monitor, and manage test security risks.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
How world-class product teams are winning in the AI era by CEO and Founder, P...
Caveon Webinar Series: Using Decision Theory for Accurate Pass/Fail Decisions
1. Upcoming Caveon Events
• Caveon Webinar Series: Next session, June 19
Protecting your Tests Using Copyright Law
• Presenters include Intellectual Property Attorney Kenneth Horton and a
member of the Caveon Web Patrol team
• Register at: http://bit.ly/protectingip
• NCSA – June 19-21 National Harbor, MD
– Dr. John Fremer is co-presenting Preventing, Detecting, and Investigating Test
Security Irregularities: A Comprehensive Guidebook On Test Security For States
– Visit the Caveon booth!
2. Latest Publications
• Handbook of Test Security – Now available for
purchase! We’ll share a discount code before end
of session.
• TILSA Guidebook for State Assessment
Directors on Data Forensics – coming soon!
3. Caveon Online
• Caveon Security Insights Blog
– http://www.caveon.com/blog/
• twitter
– Follow @Caveon
• LinkedIn
– Caveon Company Page
– “Caveon Test Security” Group
• Please contribute!
• Facebook
– Will you be our “friend?”
– “Like” us!
www.caveon.com
4. “Using Decision Theory to Score
Accurate Pass/Fail Decisions”
Lawrence M. Rudner, Ph.D., MBA
Vice President and Chief Psychometrician
Research and Development
GMAC®
May 15, 2013
Caveon Webinar Series:
Jamie Mulkey, Ed.D.
Vice President and General Manager
Test Development Services
Caveon
5. Agenda for today
• Role of decision theory
• Examples
• Logic
• Tools
• Adaptive Testing
6. Goal of Measurement Decision Theory
Classify an examinee into one of K groups
– mastery/non-master
– below basic / basic / proficient / advanced
– A / B / C / D / F
7. Poll #1
Are you involved with any classification
tests as part of your work?
Attendee Responses:
Yes – Pass/Fail – 49%
Yes - Yes - Multiple categories, e.g. A,B,C,D,F – 39%
No – 11%
8. Poll #2
How familiar are you with Item Response
Theory?
Attendee Responses:
Very – I understand and routinely apply IRT formulas – 37%
Somewhat – I understand the logic and concepts – 38%
A little – I have heard of it – 20%
Not at all – I have never heard of it – 5%
9. Poll #3
What is your primary job function?
Attendee Responses:
Teacher or Content Expert -6%
Item Writer – 8%
Psychometrician – 30%
Manager and I am a non Psychometrician – 35%
Manager and I am a Psychometrician – 21%
12. New Thinking
Probability of being a Master
or a Non-Master
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Non-Master Master
13. A Different Question
Old: Your score was 76 which is above the
passing score of 72. You passed.
vs
New: Probability of this response pattern for a
master is 85% and the probability for a non-
master is 15%. You passed.
14. IRT Approach
Probability of a correct response to Question 123 given ability level
Question 123
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
-3 -2 -1 0 1 2 3
16. Advantages
• Simple framework
• Small number of items
• Small calibration sample sizes
• Classifies as well as or better
than IRT
• Effective for adaptive testing
• Well developed science
24. Notation
• K - # of mastery states
• P(mk) - Prob of a randomly drawn examinee being
in each mastery state k
• z - an individual’s response vector z1,z2,…,zN
zi ∈ (0,1) for N questions
25. Want
P(mk | z )
The probability of each mastery state k, mk, given the
response vector z.
The probability of being a master given z
The probability of being a non-master given z
28. Mastery state
(using Bayes Theorem)
P(m | ) = P( |m ) P(m )k k kz zc
But there are too many possible response
vectors z
29. Mastery state
(using Bayes Theorem)
P(m | ) = P( |m ) P(m )k k kz zc
But there are too many possible response
vectors z
P( |m ) = P(z | m )k i k
i=1
N
z
Simplifying assumption
31. Probability of the response vector z for each
mastery state is:
P(z| m1) =.8 * .8 * (1-.6) = .26
Conditional probabilities of a correct response, P(zi=1|mk)
Item 1 Item 2 Item 3
Masters (m1) .8 .8 .6
Non-masters (m2) .3 .6 .5
Response Vector [1,1,0]
Examinee 1
32. Probability of the response vector z for each
mastery state is:
P(z| m1) =.8 * .8 * (1-.6) = .26
P(z| m2) =.3 * .6 * (1-.5) = .09
Conditional probabilities of a correct response, P(zi=1|mk)
Item 1 Item 2 Item 3
Masters (m1) .8 .8 .6
Non-masters (m2) .3 .6 .5
Response Vector [1,1,0]
Examinee 1
33. Probability of the response vector z for each
mastery state is:
P(z| m1) =.8 * .8 * (1-.6) = .26
P(z| m2) =.3 * .6 * (1-.5) = .09
Normalized
P(z| m1) = .26 / (.26 + .09) = .74
P(z| m2) = .09 / (.26 + .09) = .26
Conditional probabilities of a correct response, P(zi=1|mk)
Item 1 Item 2 Item 3
Masters (m1) .8 .8 .6
Non-masters (m2) .3 .6 .5
Response Vector [1,1,0]
Examinee 1
34. Probability of the response vector z for each
mastery state is:
P(z| m1) =.2 * .2 * .6 = .024
P(z| m2) =.7 * .4 * .5 = .14
Conditional probabilities of a correct response, P(zi=1|mk)
Item 1 Item 2 Item 3
Masters (m1) .8 .8 .6
Non-masters (m2) .3 .6 .5
Response Vector [0,0,1]
Examinee 2
35. Probability of the response vector z for each
mastery state is:
P(z| m1) =.2 * .2 * .6 = .024
P(z| m2) =.7 * .4 * .5 = .14
Normalized
P(z| m1) = .024 / (.024 + .14) = .15
P(z| m2) = .14 / (.024 + .14) = .85
Conditional probabilities of a correct response, P(zi=1|mk)
Item 1 Item 2 Item 3
Masters (m1) .8 .8 .6
Non-masters (m2) .3 .6 .5
Response Vector [0,0,1]
Examinee 2
39. Decision Rule – Maximum Likelihood
0
0.05
0.1
0.15
0.2
0.25
0.3
P(z|mk)
Master
Non-Master
• Probability of the response vector, z, for each mastery state is:
P(z| m1) = .8 * .8 * (1-.6) = .26
P(z| m2) = .3 * .6 * (1-.5) = .09
40. Decision Rule - Maximum a posteriori
probability
• Probability of each mastery state is
P(m1|z) = c * .26 *.7 = c* .52 = .87
P(m2|z) = c * .09 *.3 = c* .08 = .13
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
P(mk|z)
Master
Non-Master
41. Decision Criteria
Bayes Risk
Given a set of item
responses z and the
costs associated
with each
decision, select dk to
minimize the total
expected cost.
54. 1. Sequentially select items to maximize
certainty,
2. Administer and score item,
3. Update the estimated mastery state
classification probabilities,
4. Evaluate whether there is enough information
to terminate testing,
5. Back to Step 1 if needed.
Sequential Testing
56. Entropy
A measure of the disorder of a system.
How many bits of information are needed to send
a) 1,000,000 random signals
b) 1,000,000 zero’s
H S p pk
k
K
k( ) log
1
2
57. Less peaked = more uncertainty
= more entropy
0.0
0.2
0.4
0.6
0.8
1.0
Non-Master Master
0.0
0.2
0.4
0.6
0.8
1.0
Non-Master MasterH(s) = 1.00
H(s) = 0.72
58. Adaptive Testing
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30 35 40 45 50
Max No of items
Proportion
Accuracy
Classified
Percent classified vs accuracy as a function of the
maximum number of items administered (NAEP items)
59. Recap
• Simple framework
• Small number of items
• Classifies as well as or better than
much more complicated IRT
• Effective for adaptive testing
• Small sample sizes
• Well developed science
60. Option For
• Small certification programs
• Large certification programs
• Embedded in instructional systems
• Test preparation
61. HANDBOOK OF TEST SECURITY
• Editors - James Wollack & John Fremer
• Published March 2013
• Preventing, Detecting, and Investigating Cheating
• Testing in Many Domains
– Certification/Licensure
– Clinical
– Educational
– Industrial/Organizational
• Don’t forget to order your copy at www.routledge.com
– http://bit.ly/HandbookTS (Case Sensitive)
– Save 20% - Enter discount code: HYJ82
63. THANK YOU!
- Follow Caveon on twitter @caveon
- Check out our blog…www.caveon.com/blog
- LinkedIn Group – “Caveon Test Security”
Lawrence M. Rudner, Ph.D. MBA
Vice President and Chief Psychometrician
Research and Development
GMAC®
Jamie Mulkey, Ed.D.
Vice President and General Manager
Test Development Services
Caveon
Editor's Notes
Are you involved with any classification tests as part of your work?Yes – Pass/FailYes – Multiple categories, e.g. A,B,C,D,FNo
Are you involved with any classification tests as part of your work?Yes – Pass/FailYes – Multiple categories, e.g. A,B,C,D,FNo
Are you involved with any classification tests as part of your work?Yes – Pass/FailYes – Multiple categories, e.g. A,B,C,D,FNo
Abraham Wald (October 31, 1902(1902-10-31) - December 13, 1950) was a mathematician born in Cluj, in the then Austria–Hungary (present-day Romania) who contributed to decision theory, geometry, and econometrics, and founded the field of statisticalsequential analysis.[1]was thus home-schooled by his parents until college.[1] His parents were quite knowledgeable and competent as teachers.[2]Emigrated to US to avoid the nazi’sThomas Bayes (pronounced: ˈbeɪz) (c. 1702 – 17 April 1761) was an Englishmathematician and Presbyterian minister, known for having formulated a specific case of the theorem that bears his name: Bayes' theorem, which was published posthumously.
Shannon is famous for having founded information theory with a landmark paper that he published in 1948. However, he is also credited with founding both digital computer and digital circuit design theory in 1937, when, as a 21-year-old master's degree student at the Massachusetts Institute of Technology (MIT), he wrote his thesisdemonstrating that electrical applications of boolean algebra could construct and resolve any logical, numerical relationship. It has been claimed that this was the most important master's thesis of all time.[3] Shannon contributed to the field of cryptanalysis for national defense during World War II, including his basic work on codebreaking and secure telecommunications.