This document discusses probabilistic machine learning techniques for optimization and solving complex problems. It introduces Bayesian parametric and nonparametric models that can marginalize over weights and consider a continuous range of potential models. Gaussian processes are discussed as a way to put a prior directly on functions. Dynamic programming approaches are presented for expensive optimization problems by splitting the task, deciding next evaluation locations, and optimizing an acquisition function. Results are shown for using causality techniques to remove systematic errors in finding exoplanets from lengthy space telescope recordings with rare signals and corrupted data. The key takeaways are that probabilistic models provide confidence by knowing what they don't know, multidisciplinary teams are needed to tackle complex cases, and that theory is important to
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning, and how to utilize the TensorFlow library to rapidly build powerful ML models. You’ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges such as call center volume prediction and sentiment analysis of movie reviews. Once you’ve mastered core ML concepts, you’ll move on to the money chapters: exploring cutting-edge neural network techniques such as deep speech classifiers, facial identification, and auto-encoding with CIFAR-10. Digest this book, and you’ll be able to start modelling your everyday problems as automated machine learning tasks.
Check out the product page here: https://www.manning.com/books/machine-learning-with-tensorflow-second-edition
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning, and how to utilize the TensorFlow library to rapidly build powerful ML models. You’ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges such as call center volume prediction and sentiment analysis of movie reviews. Once you’ve mastered core ML concepts, you’ll move on to the money chapters: exploring cutting-edge neural network techniques such as deep speech classifiers, facial identification, and auto-encoding with CIFAR-10. Digest this book, and you’ll be able to start modelling your everyday problems as automated machine learning tasks.
Check out the product page here: https://www.manning.com/books/machine-learning-with-tensorflow-second-edition
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
A data science observatory based on RAMP - rapid analytics and model prototypingAkin Osman Kazakci
RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.
PyData 2015 Keynote: "A Systems View of Machine Learning" Joshua Bloom
Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I'll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products.
About: Dr. Joshua Bloom is an astronomy professor at the University of California, Berkeley where he teaches high-energy astrophysics and Python for data scientists. He has published over 250 refereed articles largely on time-domain transients events and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published recently by Princeton University Press. He is also co-founder and CTO of wise.io, a startup based in Berkeley. Josh has been awarded the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
A data science observatory based on RAMP - rapid analytics and model prototypingAkin Osman Kazakci
RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.
PyData 2015 Keynote: "A Systems View of Machine Learning" Joshua Bloom
Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I'll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products.
About: Dr. Joshua Bloom is an astronomy professor at the University of California, Berkeley where he teaches high-energy astrophysics and Python for data scientists. He has published over 250 refereed articles largely on time-domain transients events and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published recently by Princeton University Press. He is also co-founder and CTO of wise.io, a startup based in Berkeley. Josh has been awarded the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University.
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
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.
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Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
5. Empirical risk minimization
Train model through standard framework
Trading bias-variance through regularization
Bias Variance Regularized
(Crossvalidated)
Regularized + order
(Crossvalidated)
6. Point estimates
ERM is based on point estimates (one set of weights, for a fixed model)
The bias-variance trade-off pops up because we require a distinct choice.
Idea:
- consider a continuous range of potential models
- Some of them are more likely, others are not
I can live with doubt, and uncertainty. I think it's much more interesting to live not
knowing than to have answers which might be wrong.
- Richard Feynman
8. Bayesian nonparametric model: richer class of approximations
We will use the multivariate Gaussian to put a
prior directly on the function (a Gaussian process)
I can live with doubt, and uncertainty. I think it's
much more interesting to live not knowing than to
have answers which might be wrong.
- Richard Feynman
11. How to optimize?
Solving for gradient = 0?
- Too complex
- Gradient unavailable
Numerical optimization?
- Multi-modality
- Gradient unavailable
Meta-heuristics?
- Too many evaluations
- Nature took a long time to optimize
12. Dynamic programming
Let’s split the task:
- Decide location for next evaluation
- Data structure: probabilistic model
- Optimize acquisition function (sampling policy)
Goal: optimality
24. Stabilizing space telescope
Camera field can not be guaranteed(!)
Small movements cause changes in light distribution
To severe for reliable detection of earth-like plants
28. Results
Schölkopf, Bernhard, et al. "Removing systematic errors for exoplanet search via latent causes."
Proceedings of the 32nd International Conference on Machine Learning (ICML-15). 2015.
SAP represents relative flux measure
30. Take home message
AI is coming!
… enormous business potential
… but it will require more effort (time x money) than you all think
… europe is not at the forefront
(... forget about killer robots)
Theory is not to be avoided!
… without, experiments are shots in the dark
… probabilistic models know what they don’t know
… provide some confidence
Multidisciplinary teams are a must to tackle cases
… and they’ll need time
@javdrher joachim@ml2grow.com www.ml2grow.com