Algebraic machine learning (AML) is a relatively new machine learning technique based on algebraic representations of data. Unlike statistical learning, AML algorithms are robust regarding the statistical properties of the data and are parameter-free. The aim of the EU-funded ALMA project is to leverage AML properties to develop a new generation of interactive, human-centric machine learning systems. These systems are expected to reduce bias and prevent discrimination, remember what they know when they are taught something new, facilitate trust and reliability and integrate complex ethical constraints into human–artificial intelligence systems. Furthermore, they are expected to promote distributed, collaborative learning. More info at https://alma-ai.eu.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
Algebraic machine learning (AML) is a relatively new machine learning technique based on algebraic representations of data. Unlike statistical learning, AML algorithms are robust regarding the statistical properties of the data and are parameter-free. The aim of the EU-funded ALMA project is to leverage AML properties to develop a new generation of interactive, human-centric machine learning systems. These systems are expected to reduce bias and prevent discrimination, remember what they know when they are taught something new, facilitate trust and reliability and integrate complex ethical constraints into human–artificial intelligence systems. Furthermore, they are expected to promote distributed, collaborative learning. More info at https://alma-ai.eu.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Generating Synthetic Data with Generative Adversarial Networks: Opportunities and Challenges
Limited data access continues to be a barrier to data-driven product development. In this talk, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.
We identify key challenges of existing GAN approaches with respect to fidelity (e.g., capturing complex multidimensional correlations, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity).
To address fidelity challenges, we discuss our experiences designing a custom workflow called DoppelGANger and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DoppelGANger achieves up to 43% better fidelity than baseline models.
With respect to privacy, we identify fundamental challenges with both classical notions of privacy as well as recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges.
Towards XMAS: eXplainability through Multi-Agent SystemsGiovanni Ciatto
In the context of the Internet of Things (IoT), intelligent systems (IS) are increasingly relying on Machine Learning (ML) techniques. Given the opaqueness of most ML techniques, however, humans have to rely on their intuition to fully understand the IS outcomes: helping them is the target of eXplainable Artificial Intelligence (XAI). Current solutions – mostly too specific, and simply aimed at making ML easier to interpret – cannot satisfy the needs of IoT, characterised by heterogeneous stimuli, devices, and data-types concurring in the composition of complex information structures. Moreover, Multi-Agent Systems (MAS) achievements and advancements are most often ignored, even when they could bring about key features like explainability and trustworthiness. Accordingly, in this paper we (i) elicit and discuss the most significant issues affecting modern IS, and (ii) devise the main elements and related interconnections paving the way towards reconciling interpretable and explainable IS using MAS.
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
Rapid prototyping quant research ml models using the qu sandboxQuantUniversity
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
This slides is following thinking from “quick review for xAPI and IMS Caliper” (ISO/IEC JTC1 SC36/WG8 first webinar in Nov. 11, 2015). Through this slides I'm thinking two phases for mapping both data format. One is structural and syntactic mapping and the other is ontological mapping. Enjoy this trivial idea and please give my your valuable comments.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Human in the loop: a design pattern for managing teams working with MLPaco Nathan
Strata CA 2018-03-08
https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/64223
Although it has long been used for has been used for use cases like simulation, training, and UX mockups, human-in-the-loop (HITL) has emerged as a key design pattern for managing teams where people and machines collaborate. One approach, active learning (a special case of semi-supervised learning), employs mostly automated processes based on machine learning models, but exceptions are referred to human experts, whose decisions help improve new iterations of the models.
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Generating Synthetic Data with Generative Adversarial Networks: Opportunities and Challenges
Limited data access continues to be a barrier to data-driven product development. In this talk, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.
We identify key challenges of existing GAN approaches with respect to fidelity (e.g., capturing complex multidimensional correlations, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity).
To address fidelity challenges, we discuss our experiences designing a custom workflow called DoppelGANger and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DoppelGANger achieves up to 43% better fidelity than baseline models.
With respect to privacy, we identify fundamental challenges with both classical notions of privacy as well as recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges.
Towards XMAS: eXplainability through Multi-Agent SystemsGiovanni Ciatto
In the context of the Internet of Things (IoT), intelligent systems (IS) are increasingly relying on Machine Learning (ML) techniques. Given the opaqueness of most ML techniques, however, humans have to rely on their intuition to fully understand the IS outcomes: helping them is the target of eXplainable Artificial Intelligence (XAI). Current solutions – mostly too specific, and simply aimed at making ML easier to interpret – cannot satisfy the needs of IoT, characterised by heterogeneous stimuli, devices, and data-types concurring in the composition of complex information structures. Moreover, Multi-Agent Systems (MAS) achievements and advancements are most often ignored, even when they could bring about key features like explainability and trustworthiness. Accordingly, in this paper we (i) elicit and discuss the most significant issues affecting modern IS, and (ii) devise the main elements and related interconnections paving the way towards reconciling interpretable and explainable IS using MAS.
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
Rapid prototyping quant research ml models using the qu sandboxQuantUniversity
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
This slides is following thinking from “quick review for xAPI and IMS Caliper” (ISO/IEC JTC1 SC36/WG8 first webinar in Nov. 11, 2015). Through this slides I'm thinking two phases for mapping both data format. One is structural and syntactic mapping and the other is ontological mapping. Enjoy this trivial idea and please give my your valuable comments.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
International Journal in Foundations of Computer Science & Technology(IJFCST)ijfcst journal
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Human in the loop: a design pattern for managing teams working with MLPaco Nathan
Strata CA 2018-03-08
https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/64223
Although it has long been used for has been used for use cases like simulation, training, and UX mockups, human-in-the-loop (HITL) has emerged as a key design pattern for managing teams where people and machines collaborate. One approach, active learning (a special case of semi-supervised learning), employs mostly automated processes based on machine learning models, but exceptions are referred to human experts, whose decisions help improve new iterations of the models.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Machine Learning encompasses data acquisition, transmission, retention, analysis, and reduction. The expected outgrowth of 24x7 data systems and operations centers is Knowledge Engineering and Data Intensive Analytics AKA Machine Learning. This presentation will develop and apply Machine Learning concepts to the Upstream O&G industry. Specific focus will be given to the fundamental concepts and definitions of Machine Learning along with the application of Machine Learning.
Course - Machine Learning Basics with R Persontyle
This course is meant to be a fast-paced, hands-on introduction to Machine Learning using R. The course will be focusing mainly on basics of Machine Learning methods and practical implementation of these methods to solve real-world problems. This course aims to develop basic understanding of supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression.
For corporate bookings or to organize on-site training email hello@persontyle.comor call now +44 (0)20 3239 3141
www.persontyle.com
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Ed Fernandez
Adoption of ML at scale in the Enterprise, Machine Learning Platforms & AutoML
[1] Definitions & Context
• Machine Learning Platforms, Definitions
• ML models & apps as first class assets in the Enterprise
• Workflow of an ML application
• ML Algorithms, overview
• Architecture of a ML platform
• Update on the Hype cycle for ML & predictive apps
[2] Adopting ML at Scale
• The Problem with Machine Learning - Scaling ML in the
Enterprise
• Technical Debt in ML systems
• How many models are too many models
• The need for ML platforms
[3] The Market for ML Platforms
• ML platform Market References - from early adopters to
mainstream
• Custom Build vs Buy: ROI & Technical Debt
• ML Platforms - Vendor Landscape
[4] Custom Built ML Platforms
• ML platform Market References - a closer look
Facebook - FBlearner
Uber - Michelangelo
AirBnB - BigHead
• ML Platformization Going Mainstream: The Great Enterprise Pivot
[5] From DevOps to MLOps
• DevOps <> ModelOps
• The ML platform driven Organization
• Leadership & Accountability (labour division)
[6] Automated ML - AutoML
• Scaling ML - Rapid Prototyping & AutoML:
• Definition, Rationale
• Vendor Comparison
• AutoML - OptiML: Use Cases
[7] Future Evolution for ML Platforms
Appendix I: Practical Recommendations for ML onboarding in the Enterprise
Appendix II: List of References & Additional Resources
Best Data Science Hybrid Course in Pune
Data Science, in its simpler terms, is about generating critical business value from the data through various creative ways. It can also be defined as a mix of data research, algorithms, and technology to solve complex analytical issues. Data is being generated by Companies at an exponential pace. The usable Data form can be different for different sections of people working in an organization.
Data Science Classes help us to explore the data to a granular form and find the needed insights. Data Science is about being analytical or inquisitive wherein asking new questions, doing further explorations, and continuing learning is a part of the job for Data Scientists.
According to Harward Business Review, Data Scientist is the Sexiest Job of the 21st Century.
According to Forbes, IBM Predicts Demand For Data Scientists Will Soar 28% By 2020
GET FRONTLINE DATA SCIENCE TRAINING IN PUNE AT 3RI TECHNOLOGIES
Data Science is a trending niche, for it promises notable mileage for the business economy! It is rather ironic that data which was considered a burden to manage and store only about a few decades ago is now viewed as a resource; courtesy of course to data scientists. They have brought about a paradigmatic change through their skills which allow them to derive the value from raw data. It is important to mention that ‘Raw Data’ is clueless to most laymen, including the high echelons in business management; but when processed through Data Science Tools, it renders value that is precious and immense for the decision-makers and salesmen. They are all riding on the Professionalism of the Data Scientists and this generates the demand of the latter! 3RI Technologies is the leading institution offering Data Science Classes in Pune and fresh graduates as well as Working Professionals can enroll for it.
WHAT IS DATA SCIENCE?
Today, Data Science is a much-talked subject and its significance is being deliberated among the business managers who are eager to hire a brilliant professional onboard their firm. Data Science is a milieu space that is shared by the distinct yet related domains of statistics & applicative mathematics, computer programming frameworks and tools, data metrics, and analytics. Machine Learning & associated automation underpins all the above-listed fields, almost as a generic derivative; because it is through this channel that the good results are accrued in favor of the business clients. What are these good results? Let’s talk about them!
Trending smart services that are propelling businesses around the world such as SEO, SMO, SMM, SEM and CRM, all revolve around the ability to generate leads of authentic value for the commerce banners. The web developers have been doing well through their professional conduct for their clients but they in turn actively seek the ‘Meaningful Data’ about the existing and potential customers, the market trends, and the competition figures of the biz rivals. Here, Data Sc
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
4. Attempt at definition
Machine Learning
• “… gives computers the ability to learn without being explicitly
programmed” (Arthur Samuel, 1959)
• “… is the systematic study of algorithms and systems that improve their
knowledge or performance with experience” (Peter Flach, 2012)
• “… concerns systems that automatically learn programs from data” (Pedro
Domingos, 2012)
14-10-2019 page 4
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
5. Related to ML
• Artificial Intelligence
• Knowledge discovery
• (Predictive) Analytics
• Statistics / Statistical Learning
• Optimization
• Evolutionary algorithms
• Deep Learning
• Data Mining
• Pattern recognition
• Data Science
• Informatics, computer/computational science
• Econometrics
• Related buzzwords: Big Data, Internet of Things
14-10-2019 page 5
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
6. Related fields
Artificial Intelligence
Data Science
Statistics
Informatics
Econometrics
Optimization
14-10-2019 page 6
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
7. Terminology
Statistics/Econometrics Machine Learning
Independent variables, predictors Features, inputs
Dependent variable Output, response
Estimation, fitting Training, learning
Dummy coding One-hot encoding
Transformation of variables Feature engineering
Parameters Weights
Regression/classification Supervised learning
Goal is to understand (model) Goal is to predict
14-10-2019 page 7
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
8. ML Applications
• Handwriting recognition
• Facial/image recognition
• Speech recognition
• Spam filters
• Text Mining
• DNA sequence classification
• Search engines
• Stock market analysis
• Game playing
• Medical diagnostics
• Fraud detection
• Passenger screening
• Crime prediction
• Satellite image classification
• Robotics
• Automatic flight pilots
• Self-driving cars
• …
14-10-2019 page 8
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
11. “Object” recognition
in computer vision
14-10-2019 page 11
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
12. ML Techniques
Three groups:
• Supervised learning (classification, regression)
• Unsupervised learning (PCA, clustering, …)
• Reinforcement learning (agent-based)
• (Transfer learning)
• …
What do you need for a self-driving car?
14-10-2019 page 12
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
13. Supervised techniques
Specific for Classification
• Decision trees
• Bagged trees
• Boosted trees
• Random Forests
• Neural networks
• Support Vector Machines
• Genetic programming
• Bayesian Networks
• MARS
• Lasso
• Logistic regression
• Naive Bayes
• kNN
• Ensemble models
• …
14-10-2019 page 13
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
16. Example Unsupervised:
Anomaly detection
boundary case
outlier
extreme
case
Robust regression:
MVE estimation
14-10-2019 page 16
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
17. Optimisation techniques
• Linear programming
• (Mixed) Integer Programming
• Non-linear programming
• Modern optimisation techniques:
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Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
18. Considerations
• Similarity between ML and statistical methods is often big:
representation – evaluation – optimisation
• Personal note: if it works well (prediction!), use it!
(but explainability may also matter)
• Data preparation (incl. feature engineering) often is 80% of the work
• Bias-Variance dilemma remains (overfitting)
• Perform fair comparison using ROC-curves on independent testset
• “No free lunch”: no single technique is always best
=> Use expert knowledge and choose representation fitting the problem
(data alone is not enough)
• Curse of dimensionality: input space grows exponentially with k, the
number of observations (generally) does not
• Consider making multiple models and combining (ensembles)
14-10-2019 page 18
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
19. Sources
• Literature (use Google Scholar and arXiv)
• Data (Kaggle, UCI, Quandl, governments, APIs)
• Competitions (Kaggle, Topcoder, HackerRank, CrowdAnalytix)
• Courses (Coursera, Udacity, Udemy, DataCamp)
• Academic education (A’dam School of Data Science,
Eindhoven, Delft, Tilburg)
• Fora (Kaggle, Stackoverflow, Quora)
• Other websites (Analytics Vidhya, Data Science Central,
DeepMind, DutchDigitalDelta-Commit2data)
•
14-10-2019 page 19
s (e.g. ADS and AMDS)
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
20. Literature
• Articles
– A Few Useful Things to Know About Machine Learning (Pedro Domingos, CACM Oct 2012)
– Statistical Modeling: The Two Cultures (Leo Breiman, Statistical Science 2001)
• Books
– The Elements of Statistical Learning (Hastie/Tibshirani/Friedman; Springer
2008)
– Applied Predictive Modeling (Kuhn/Johnson; Springer 2013)
– Machine Learning (Flach; Cambridge Univ. Press 2012)
– Reinforcement Learning: An Introduction (Sutton/Barto; MIT Press 2012)
– Artificial Intelligence: A Modern Approach 3rd ed. (Russell/Norvig; Prentice Hall
2016)
– Modern Optimization with R (Cortez; Springer 2014)
14-10-2019 page 20
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
21. Software
• General purpose programming languages
– Python
– R
– SAS
– Matlab
• ML environments/libraries
– MS Azure
– Google Tensorflow (for R)
– AWS: Amazon Web Services
14-10-2019 page 21
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
22. Machine Learning in R
• CRAN Task View:
Machine Learning & Statistical Learning
• Caret package
– Vignette
– Many model types
– Training and prediction
– Variable importance
– Parameter tuning
– Cross-Validation, ROC curves, plots
– etc.
• Tensorflow interface (via Python)
14-10-2019 page 22
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
23. Machine Learning in Python
14-10-2019 page 23
Definition
Applications
Techniques
Considerations
Sources
Software
ML in R & Python
• scikit-learn
• pytorch
(for deep learning)
• (auto-ml)
24. Reinforcement Learning
• MDP: Markov Decision Process
• Environment (S,A,P,R) entirely or partly known
• Packages in R
– MDPtoolbox
– ReinforcementLearning
• Code in Python
– Lots on github,
e.g. DeepMind TRFL
• Self coding
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