ODSC East 2018 (Open Data Science Conference)

ODSC East 2018 (Open Data Science Conference)

by Open Data Science Conference
Released September 2018
Publisher(s): Addison-Wesley Professional
ISBN: 0135432790

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Video description

ODSC

The Open Data Science Conference has established itself as the leading conference in the field of applied data science. Each ODSC event offers a unique opportunity to learn directly from the core contributors, experts, academics and renowned instructors helping shape the field of data science and artificial intelligence

Presentations cover not only data science modeling but also the languages and tools needed to deploy these models in the real world such as TensorFlow, MXNet, scikit-learn, Kubernetes, and many more.

Our conferences are organized around focus areas to ensure our attendees are at the forefront of this fast emerging field and current with the latest data science languages, tools, and models. You’ll find in our East 2018 video catalog some of our most popular focus areas including:

Deep Learning and Machine Learning

Over the last 5 years, we have seen incredible advances in the field of data scientist thanks to breakthroughs in neural networks, transfer learning, reinforcement learning, and generative adversarial networks (GANs) to name a few. With the advent of Google Voice, Alexa, and other voice assistants, presentations on enabling technologies like NLP, RNNs, and LSTM are popular. Some session to note:

  • Distributed TensorFlow using Kubernetes
  • Deep Learning Pipelines for Big Images
  • Machine Learning and Natural Language Processing for Detecting Fake News
  • Long-Term Time Series Forecasting with Recurrent Neural Networks
  • A Breakthrough for Natural Language
  • Transfer Learning: Applications for natural language understanding
  • Challenges and Opportunities in Applying Machine Learning
  • Effective Transfer Learning for NLP
  • Developing Machine Learning Solutions with Plugin Machine Intelligence for PDI
  • Distributed Tensorflow: Scaling Your Model Training

Core Data Science and Data Visualization

As data science advances at a rapid pace, core skills are more important than ever. Our sessions range from beginner to advanced level for core topics. Additionally, data and models need to be actionable and data visualization remains a key skill in any data scientist's toolkit. Some session of note include:

  • Bayesian Statistics Made Simple
  • Gradient Descent, Demystified
  • Comparing Models Using Resampling and Bayesian Methods
  • Next Generation Indexes For Big Data Engineering
  • Probabilistic Programming with PyMC3
  • Racial Bias in Facial Recognition Software
  • Visualization throughout the Data Science Workflow:
  • Datafy All The Things

Data Science, Management, And Business

Data science is permeating every industry as adoption gathers pace. The management and practice of data science will become increasingly strategically important to all industries including finance and healthcare. Hear from leading experts on important topics including:

  • Blockchain and AI: future data systems must be built differently
  • Marketing in a Machine Learning World
  • The Adoption of AI in Business: Opportunities and Challenges:
  • Winning with AI: What's working, what needs work?
  • Building an effective AI practice
  • Applied Finance: The Third Culture
  • Machine Learning Powers Better Decisioning in Financial Services
  • AI and Data Science in Investment
  • AI and Big data in Medicine: Trust, Transparency, and Transformation
  • Algorithmic Transparency and Health Care: Where do we go from here?
  • What will you do with democratized health data?
  • A Physician-Data Scientist Grand Vision: A Virtual Medical Oracle

Thought Leadership | Keynotes

Data science and artificial intelligence are helping shape the future of business and society. Thoughtful leadership is essential and ODSC East was honored to host a few of the field's leading lights including Cathy O’Neil, author of Weapons of Math Destruction, Drew Conway, one of the most well-known data scientists in the world, Gary Marcus, an award-winning Professor, and Kirk Borne, data scientist and executive advisor at Booz-Allen Hamilton. Thought-provoking presentations include:

Catherine O'Neil | Weapons of Math Destruction: Separating Data Facts and Opinion
Drew Conway | Building a Data Science Company
Kirk Borne | Current and Future Trends in AI, Machine Learning, and Data Science
Gary Marcus | The Social Disease Known as Hype in AI and How to Separate What AI can do and What People Wish it Would do

Please see our table of contents for a full list of videos

Table of contents

  1. Keynotes
    1. The Social Disease Known as Hype in AI and How to Separate What AI can do and What People Wish it Would do—Gary Marcus
    2. Current and Future Trends in AI, Machine Learning, and Data Science—Kirk Borne
    3. Weapons of Math Distraction: Separating Data Facts and Opinion—Catherine O'Neil
    4. Building a Data Science Company—Drew Conway
  2. Data Science
    1. Towards Identity Resolution: The Challenge of Name Matching—Gil Irizarry
    2. Uplift Modeling for Driving Incremental Revenue by Display Remarketing—Jen Wang
    3. Using Data for Good—Brandon Rohrer
    4. Collaborative Data science and How to Build a Data science Toolchain Around Notebook Technologies—Moonsoo Lee
    5. Known Unknowns: Designing uncertainty into AI-powered systems—Sean Kruzel
    6. Recommending the best hotels at TripAdvisor—Anyi Wang
    7. The Hamiltonian Monte Carlo Revolution is Open Source: Probabilistic Programming with PyMC3—Austin Rochford
    8. Comparing Models Using Resampling and Bayesian Methods—Max Kuhn, PhD
    9. Distributed Tensorflow: Scaling Your Model Training—Neil Tenenholtz
    10. Racial Bias in Facial Recognition Software—Stephanie Kim, Hillary Green-Lerman
    11. Making Sense of the Biomedical Literature via Machine Learning and Natural Language Processing—Byron Wallace
    12. Bayesian Statistics Made Simple—Allen Downey
  3. Data Vizualization
    1. Visualization throughout the Data Science Workflow: Why it's useful, and how not to lie—Lindsay Brin, PhD
    2. Analyzing Space: Spatial Data Science...—Andy Eschbacher
    3. Datafy All The Things—Max Humber
  4. Deep Learning / Machine Learning
    1. Enter the Matrix: Unsupervised feature learning with matrix decomposition to discover hidden knowledge in high dimensional data—Aedin Culhane, PhD
    2. Learning-to-learn: an Overview—Jennifer Prendki
    3. Next Generation Indexes For Big Data Engineering—Daniel Lemire
    4. Artificial Intelligence ‚Äì a journey to surpass the Turing test—Anisha Baidya, Michael Commons, Mansi Shah
    5. Deep Learning Pipelines for Big Images—Michael Segala
    6. Distributed TensorFlow using Kubernetes—Sertac Ozercan, Rita Zhang
    7. Effective Transfer Learning for NLP—Madison May
    8. Long Term Time Series Forecasting with Recurrent Neural Networks—Mustafa Kabul
    9. Developing Machine Learning Solutions with Plugin Machine Intelligence for PDI—Kevin Haas, Dave Huh
    10. Challenges and Opportunities in Applying Machine Learning—Alex Jaimes
    11. Deploying your AI/ML investments—Jon Peck
    12. Gradient Descent, Demystified—Michael Stewart
    13. How AI-Powered Natural Language Processing of Social Data is Fueling a New Generation of Predictive Analysis—Rob Key, Mark Garrett
    14. Machine Learning for Mobile Sensing Applications—Michael Bell, PhD
    15. Try All the Things! Liberate Users while Reducing Risks in Open Source Data Science—Jordan Volz
    16. Making the Most of Your Time Series: Signal Processing for Machine Learning applications—Keith Santarelli, Eric Schles
    17. Exploiting Multi-class Probabilities for solving Network Security Anomalies using Supervised and UnSupervised Machine Learning Approaches—Ashrith Barthur, PhD
    18. Machine Learning and Natural Language Processing for Detecting Fake News—Sihem Romdhani
    19. Privacy and Machine Learning : Peanut Butter and Jelly—Steve Touw
    20. TLDR ‚Äì automatic text summarization of documents at scale—Guilherme de Oliveira, PhD
    21. To Bid or Not To Bid: Machine Learning in Ad Tech—Justin Fortier
    22. A Breakthrough for Natural Language—Ben Vigoda
  5. Data Science Management
    1. Data Science State of the Union, 2018—Peter Wang
    2. How to Go From Data Science to Data Operations—Gil Benghiat
    3. Making Business More Bayesian—Richard Tibbetts
    4. DataOps: Enterprise Data that Doesn't Suck—Andrew Palmer
  6. Business and Management
    1. Blockchain and AI: future data systems must be built differently—Professor Alex Pentland, MIT
    2. Look Who's Talking: A Deep Dive into the Medium of the Future ‚Äî Natural Language Conversation—Eyal Pfeifel
    3. Marketing in a Machine Learning World—Drew Casey
    4. The Adoption of AI in Business: Opportunities and Challenges:—Sam Ransbotham
    5. Transfer Learning: Applications for natural language understanding—Dr. Catherine Havasi
    6. Using Unstructured Data and Machine Learning to Understand Loss Events—Niranjan Thomas
    7. Winning with AI: What's working, what needs work?—Manoj Saxena
    8. Building an effective AI practice—Ram Ravichandran
    9. AI and the World of Non-Profits—Rich Palmer
    10. Building a Big Data Center Of Excellence—The Secret Sauce to your Data Journey ‚Äî The People.—Amy Cloudera
  7. Business and Management: Finance
    1. A viral model for scalable adoption of data science in a large financial organization?—Antonio Alvarez
    2. Applied Finance: The Third Culture—Steve Lawrence
    3. Mission Analytics: Common pitfalls and how to avoid them (A journey in an insurance company)—Delin Shen
    4. Extracting Embedded Alpha from Social News Data Using Statistical Arbitrage Machine Learning—Arun Verma
    5. Knowledge Graphs in Financial Technology—Future or Hype—Tomasz Adamusiak
    6. Machine Learning Powers Better Decisioning in Financial Services—Alexander Statnikov
    7. AI and Data Science in Investment—Kazhuri Shimbo
  8. Business Management: Healthcare
    1. Algorithmic Transparency and Health Care: Where do we go from here?—Norma Padr√≥n
    2. Product-Data Fit: The Lean Startup Methodology and Healthcare Data Products—Daniel Shenfeld, Afik Gal
    3. AI and Big data in Medicine: Trust, Transparency and Transformation—Lynda Chin
    4. What will you do with democratized health data?—QuHarrison Terry
    5. A Physician-Data Scientist Grand Vision: A Virtual Medical Oracle—Anthony Chang
    6. Harnessing the power of recommender system for drug off-target activity prediction—Ambrish Roy

Product information

  • Title: ODSC East 2018 (Open Data Science Conference)
  • Author(s): Open Data Science Conference
  • Release date: September 2018
  • Publisher(s): Addison-Wesley Professional
  • ISBN: 0135432790