Virtual and In-Person | October 27th – 30th, 2020

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the creators and top practitioners behind two of data science’s hottest topics
Register now | 30% ends soon

Machine Learning & Deep Learning

The Machine Learning & Deep Learning Track is where experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.

Attend talks, tutorials, and workshops and hear from the creators and top practitioners as they demonstrate and teach the latest models and trends in Machine Learning and Deep Learning to solve problems in business and society.

Some Current Speakers

deep learning with TensorFlow
Chief Data ScientistUntapt

Dr. Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

Deep Learning (with TensorFlow 2)(Full-Day Training)

Machine Intelligence ScientistDraper

Teal Guidici, PhD

Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.

Echo State Networks for Time-Series Data (Tutorial)

Founder & Chief Scientist, ProfessorPetuum, Carnegie Mellon University

Eric Xing, PhD

Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the Carnegie Science Award, National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; and he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.

Head of Data Science and AnalyticsBadoo (now MagicLab)

Leonardo De Marchi

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving. He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.

Modern and Old Reinforcement Learning Part 1(Training)

Modern and Old Reinforcement Learning Part 2(Training)

Chief Data Evangelist, Director of Data ScienceCisco Systems, The Erika Legacy

Jennifer Redmon

Jennifer Redmon joined Cisco in 2009 and serves as its Chief Data Evangelist. She and her team support Cisco’s journey to up-level the company’s analytical acumen through offers such as education. In 2019, she joined The Erika Legacy Foundation pro bono as its Director of Data Science and Artificial Intelligence to honor the foundation’s namesake, Erika, with whom Jennifer had been close friends prior to Erika’s death. Jennifer is passionate about ethical applications of data science, ML/AIs power to create a more compassionate world, DIY home projects, Peloton, and travel. Jennifer holds an international MBA from Duke University with a concentration in Strategy and Bachelor’s Degrees in Economics and Art History from UC Davis.

Data Science for Suicide Prevention(Talk)

Data Science ManagerCisco Data Science Lab

Dr. Annie Ying

Annie is a leader in data science with over a decade of industry experience spanning corporate research and startups. Currently, she is the head of the Cisco Data Science Lab in Vancouver. Coming from a research background with a PhD in Computer Science from McGill, Annie is a former Research Scientist at IBM T. J. Watson Research Center in New York and a two-time winner of an ACM Distinguished Paper Award (both in applying data science to software engineering) with five patents (granted and applied). Annie is active in the data science community as a Meetup organizer (Data Science for Social Good), speaker, and mentor.

Data Science for Suicide Prevention(Talk)

 

Principal Software Engineer, AuthorDomino Data Lab

Dean Wampler, PhD

Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of “Programming Scala”, “What Is Ray?”, “Fast Data Architectures for Streaming Applications”, “Functional Programming for Java Developers”, and the coauthor of “Programming Hive”, all from O’Reilly. He is a contributor to several open source projects, a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.

Hands-on Reinforcement Learning with Ray RLlib(Half-Day Training)

Principal Data ScientistLinc Global

Joan Xiao, PhD

Joan Xiao is a Principal Data Scientist at Linc Global, a commerce-specialized customer care automation company. In her role, she applies novel natural language processing and machine learning techniques to improve customer experience. Previously she led machine learning and data science teams at various companies ranging from startup to Fortune 100. Joan received her Ph.D in Mathematics and MS in Computer Science from University of Pennsylvania.

Transfer Learning in NLP(Talk)


Sessions on Machine Learning & Deep Learning Track

  • Training: Natural Language Processing with PyTorch

  • Training: State of the art AI Methods with TensorFlow: Transfer Learning, RL and GANs

  • Training: Deep Learning (with TensorFlow 2)

  • Training: Hands-on Reinforcement Learning with Ray RLlib

  • Training: Modern and Old Reinforcement Learning

  • Workshop: A Hands-On Tutorial for Training Interpretable Variational Autoencoders Using siVAE

  • Talk: Ludwig, a Code-Free Deep Learning Toolbox

  • Workshop: Solving Problems with Both Text and Numerical Data Using Gradient Boosting

  • Talk: Data Science for Suicide Prevention

  • Talk: The Bayesians are Coming! The Bayesians are Coming, to Time Series

  • Workshop: StructureBoost: Gradient Boosting with Categorical Structure

  • Talk: Beyond OCR: Using Deep Learning to Understand Documents

What You'll Learn

Talks + Workshops + Special Events on these topics:

Topics

  • Machine Learning

  • Deep Learning

  • Artificial Intelligence

  • Neural Networks

  • Natural Language Processing

  • Computer Vision

  • Pattern Recognition

Tools & Languages

  • R

  • Python SciPy, Pandas, etc

  • Scikit-learn

  • Tensorflow

  • Spark

  • MLlib

  • H20

Tools & Languages

  • WEKA

  • Pylearn2

  • Theano

  • Caffe

  • Torch

  • Azure Machine Learning API

  • and many more..

Why Attend?

Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends

Expand your network and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career

Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning

Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and impacting the domains of business, society, healthcare, finance, manufacturing, and more

Sign Up for ODSC WEST | October 27th – 30th, 2020

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