April 20th - 21st, 2022

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
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    Comprising multiple tracks, this focus area is where leading 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 teach the latest models and trends in Machine Learning and Deep Learning to solve problems in business and society. Some of the topics you’ll learn include:

    • Machine Learning

    • Deep Learning

    • Deep Reinforcement Learning

    • Neural Networks

    • LSTM, CNNs, RNNs, & GANs,

    • Computer Vision

    • Pattern Recognition

    • Tensorflow

    • Scikit-learn

    • Keras

    • Caffe 2

    • PyTorch

    • Theano

    • Apache Spark & MlLib

    • and many more…

    • Federated Learning

    • Transfer Learning

    • Autonomous Machines

    • MLOps and Kubeflow

    • Recommendation Systems

    • Never Ending Learning for ML

    • Causal Inference

    Some Current ML & DL Speakers

    Kirk borne
    Principal Data ScientistBooz Allen Hamilton

    Dr. Kirk Borne

    Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

    Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Half-Day Training)

    Atypical Applications of Typical Machine Learning Algorithms(Half-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)

    Senior Software EngineerQuansight Lab

    Thomas Fan

    Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.

    Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

    Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

    Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

    Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

    Professor of Machine Learning, AI, and Medicine, Director, Professor of Electrical and Computer EngineeringUniversity of Cambridge, UCLA

    Mihaela van der Schaar, PhD

    Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

    Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

    Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.

    In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

    Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

    In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

    Why Medicine is Creating Exciting New Frontiers for Machine Learning(Keynote)

    Co-Founder, Principal Data Scientist & FacultyRoboflow.com, BetaVector, General Assembly

    Joseph Nelson

    Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded. He’s easily reached on Twitter: @josephofiowa

    Applied Deep Learning: Building a Chess Object Detection Model with Computer Vision(Half-Day Training)

    Developer Advocate, TensorFlowGoogle

    Robert Crowe

    A recovering data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive.

    From Experimentation to Products: The Production ML Journey(Talk)

    Vice President of Data AnalyticsOptum

    Margaret Good, PhD

    Margaret (Meg) Good, Ph.D., specializes in health economics, health policy, and survey research methods. She has been with Optum since 2005. In her current role as Vice President of Data Analytics, Dr. Good advises Optum businesses on how to use analytics to achieve strategic objectives for their products and services. She supports the advancement and use of artificial intelligence, machine learning, advanced analytics, and emerging technologies at Optum. Before joining the OEA team, Dr. Good served as the Vice President of Health Economics & Outcomes Research in Optum Life Sciences. This team conducts observational research studies using administrative claims data, patient and provider surveys, EHR/medical chart data, and other secondary data sources. Prior to joining Optum, she was a faculty member in the Department of Public Policy at the University of Maryland, Baltimore County where she taught courses in health policy and research methods. She also worked at the University of Minnesota where she worked in a research collaborative funded by the Robert Wood Johnson Foundation to help states expand access to health insurance and health coverage among disadvantaged populations. Dr. Good earned her PhD and MS in health services research and policy at the University of Minnesota and her undergraduate degree at Williams College. She has presented her research at national conferences and has authored or co-authored publications that include articles in the Journal of the American Medical Association, Inquiry, Medical Care Research & Review, and the Journal of Health Politics, Policy and Law.

    Responsible Use of AI in Health Care(Talk)

    Professor, Lab Director, Sr. AI ScientistCornell Tech, Rochester Institute of Technology, Paige

    Christopher Kanan, PhD

    Christopher Kanan is an Assistant Professor at the Rochester Institute of Technology (RIT), a Visiting Assistant Professor at Cornell Tech, and a Senior AI Scientist at Paige. At RIT, his lab works on lifelong machine learning and language driven computer vision, which has been supported by awards from NSF, AFOSR, ONR, DARPA, Adobe Research, and other industrial partners. He is also Associate Director of RIT’s Center for Human-aware AI and he is a member of RIT’s McNair Scholars advisory board. At Paige, a startup that has raised $95M to improve the diagnosis of cancer, he led the AI R&D team during its first 1.5 years and continues to advise its AI teams. He received a PhD in computer science from the University of California at San Diego, where he worked on brain-inspired algorithms for object recognition, neural networks, active vision, and cognitive modeling. He received an MS in computer science from the University of Southern California. Before joining RIT, he was a postdoctoral scholar at the California Institute of Technology, and later worked as a Research Technologist at NASA’s Jet Propulsion Laboratory, where he used deep learning to develop vision systems for autonomous ships. He is the recipient of the 2016 Rising Star Award and the 2019 Distinguished Scholarship Award in RIT’s College of Science. He is an IEEE Senior Member and has published over 50 refereed papers, many of which are in top venues across AI such as CVPR, ICCV, NeurIPS, AAAI, ICLR, ACL, etc.

    Continual Learning in Deep Neural Networks: Methods and Applications(Tutorial)

    Principal AI ScientistZS Associates

    Srinivas Chilukuri

    Srinivas leads ZS AI Research Lab with a focus on frontier innovation and development of cutting edge algorithms. Srinivas’s core expertise areas include automated machine learning, natural language processing, and marketing AI across industries. He has authored several thought leadership articles and presented at conferences. Prior to joining ZS, Srinivas spent time as a solution architect building expert systems to automate product design and manufacturing across multiple industries viz., automobile, power systems, medical devices and retail

    Improving Structured Data Ml Processes with Generative Adversarial Networks(Business Talk)

    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.

    Linear Algebra, Calculus, and Probability: The Math ML Experts Master(Tutorial)

    Senior Data ScientistGreenhouse Software

    Mona Khalil

    Mona is a Senior Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

    Evaluation of Statistical Models based on Stakeholder Needs(Tutorial)

    Assistant Professor of Machine LearningEindhoven University of Technology

    Joaquin Vanschoren, PhD

    Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.

    Tutorial on Automated Machine Learning(Workshop)

    Assistant ProfessorUniversity of Wisconsin-Madison

    Sharon Yixuan Li, PhD

    Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Previously she spent a wonderful year as a postdoc researcher in the Computer Science department at Stanford University, working with Chris Ré. Sharon Yixuan Li completed her PhD from Cornell University in 2017, where Sharon was fortunate to be advised by John E. Hopcroft. Her thesis committee members are Kilian Q. Weinberger and Thorsten Joachims. Sharon has spent time at Google AI twice as an intern, and Facebook AI as a Research Scientist. Sharon was named Forbes 30 Under 30 in Science in 2020.

    Sharon Yixuan Li broad research interests are in deep learning and machine learning. Her time in both academia and industry has shaped her view and approach in research. The goal of the research is to enable transformative algorithms and practices towards reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Our works explore, understand, and mitigate the many challenges where failure modes can naturally occur in deploying machine learning models in the open world.

    Reliable Open-World Learning Against Out-of-distribution Data(Talk)

    Research Scientist and ManagerFacebook

    Yuandong Tian, PhD

    Yuandong Tian is a Research Scientist and Manager in Facebook AI Research, working on deep reinforcement learning in games and theoretical analysis of deep models. He led OpenGo, a super-human Go bot from Facebook AI. Prior to that, he was a Software Engineer/Researcher in Google Self-driving Car team during 2013-2014. He received Ph.D in Robotics Institute, Carnegie Mellon University on 2013, Bachelor and Master degree of Computer Science in Shanghai Jiao Tong University. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions.

    Learning to Optimize High-Dimensional Optimization Problems(Talk)


    Some Previous ML & DL Speakers

    Director of Data Analytics and AI SolutionsGoogle Cloud

    Valliappa Lakshmanan, PhD

    Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud’s data analytics and machine learning products. He founded Google’s Advanced Solutions Lab ML Immersion program and is the author of three O’Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Follow him on Twitter at @lak_gcp, read articles by him on Medium, and see more details at www.vlakshman.com

    Practical Machine Learning on Images(Half-Day Training)

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    Data Science AdvocateNeo4j

    Dr. Clair J. Sullivan

    Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. In 2021 she joined Neo4j as a Graph Data Science Advocate. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.

    Machine Learning With Graphs: Going Beyond Tabular Data(Talk)

    Director | Associate Professor Statistical Machine Learning Lab | UCLA

    Quanquan Gu, PhD

    Quanquan Gu is an Assistant Professor of Computer Science at UCLA and the director of the statistical machine learning lab. His research is in the area of artificial intelligence and machine learning, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning to understand large-scale, dynamic, complex, and heterogeneous data and building the theoretical foundations of deep learning and reinforcement learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award, NSF CAREER Award, Simons Berkeley Research Fellowship among other industrial research awards. He leads a team at UCLA using machine learning to forecast the spread of COVID-19 (https://covid19.uclaml.org) and their model has been adopted by the U.S. Centers for Disease Control and Prevention and the California Department of Public Health.

    Statistical Machine Learning(Talk)

    DirectorNVIDIA

    Magnus Ekman, PhD

    Magnus Ekman is a Director of Architecture at NVIDIA, where he leads an engineering team working on CPU performance and power efficiency. As the deep learning (DL) field exploded in the past few years, fueled by NVIDIA’s GPU technology and CUDA, he found himself in the midst of a company expanding beyond computer graphics and becoming a DL powerhouse. As a part of that journey, he challenged himself to stay up to date with the most recent developments in the field. In collaboration with NVIDIA Deep Learning Institute (DLI) he recently published the book “Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow.”

    Introduction to DL-based Natural Language Processing using TensorFlow and PyTorch(Workshop)

    Director | Associate Professor StarAI (Statistical and Relational Artificial Intelligence Lab) | UCLA

    Guy Van den Broeck, PhD

    Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.

    Machine Learning LeadToyota Research Institute

    Adrien Gaidon

    Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research – Inria Paris in 2012, has over 50 publications and patents in ML & Computer Vision (cf. Google Scholar), and his research is used in a variety of domains, including automated driving. You can find him at adriengaidon.com, on linkedin, and Twitter @adnothing.

    Self-Supervised 3D Vision(Talk)

    Training and Enablement EngineerDomino Data Labs

    Andrea Lowe, PhD

    Andrea Lowe, PhD is the Training and Enablement Engineer at Domino Data Labs where she develops training on topics including overviews of coding in Python, machine learning, Kubernetes, and AWS. She trained over 1000 data scientists and analysts in the last year. She has previously taught courses including Numerical Methods and Data Analytics & Visualization at the University of South Florida and UC Berkeley Extension. Her conference experience includes a deep learning tutorial at PyCon, 2 invited talks, 21 poster presentations, and 4 chair positions.

    Practical Reinforcement Learning for Data Scientists(Workshop)

    Developer Relations Engineering ManagerGoogle

    Karl Weinmeister

    Karl Weinmeister is a Developer Relations Engineering Manager at Google, based out of Austin, Texas. Karl leads a global team of data science and ML engineering experts in the Developer Advocacy organization, who build technical assets and consult with enterprise customers on Artificial Intelligence and Machine Learning. Karl was a contributor to Proverb, an AI-based crossword puzzle solver, which competed at the American Crossword Puzzle Tournament.

    Get Started with Time-Series Forecasting using the Google Cloud AI Platform(Workshop)

    Staff Field Data ScientistDomino Data Labs

    Jennifer Dawn Davis, PhD

    Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level.

    Practical Reinforcement Learning for Data Scientists(Workshop)

    Sr. Data Science InstructorGalvanize, Inc.

    Ryan Kasichainula

    Ryan Kasichainula is a data science instructor at Galvanize, Inc, an industry leader in technology education, with data science and software engineering immersive bootcamps. They are also an independent data consultant with experience in the technology, agriculture, energy, and pharmaceutical industries. Ryan enjoys applying data science techniques to a wide variety of domains, and they always have at least one side project in the works, usually in the realm of natural language generation.

    Sound Classification and Detection with STFT and CNNs(Workshop)

    Senior Data Scientist | Data Science InstructorGalvanize Inc (part of Stride)

    Rongpeng Li

    Ron Li is a data science instructor and senior data scientist at Galvanize, Inc. Before that, He worked on machine learning and knowledge graphs at the Information Sciences Institute. Ron has published a 4.5-star rating book Essential Statistics for Non-STEM Data Analysts. He has also authored/co-authored several academic papers, taught data science to non-STEM professionals as pro bono service, and gave talks at conferences like PyData.

    A Complete Real-Time Data Application in 90 Minutes : from Kafka to Streamlit(Workshop)

    Research ScientistFacebook AI Research

    Adriana Romero

    Adriana Romero Soriano is a research scientist at Facebook AI Research and an adjunct professor at McGill University. Her research focuses on developing models and algorithms that are able to learn from multi-modal data, reason about conceptual relations, and leverage active acquisition strategies to mitigate their uncertainties. The playground of her research has been defined by problems that require inferring full observations from limited sensory data. She completed her postdoctoral studies at Mila, where she was advised by Prof. Yoshua Bengio. Her postdoctoral research revolved around deep learning techniques to tackle biomedical challenges, such as the ones posed by multi-modal data, high dimensional data, and graph-structured data. She received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks, advised by Dr. Carlo Gatta.

    Seeing the Unseen: Inferring Unobserved Information from Limited Sensory Data(Talk)

    Software Developerembarc Software Consulting GmbH

    Oliver Zeigermann

    Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.

    Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0(Half-Day Training)

    Data Scientist | FounderThe Crosstab Kite

    Brian Kent, PhD

    Brian Kent is the founder of The Crosstab Kite, a publication for professional data scientists solving real-world challenges. He writes about survival analysis, data-driven decision-making, data science tools, and big picture trends in statistical modeling. Prior to The Crosstab Kite, Brian worked in the FinTech space as Director of Data Science & Machine Learning at Credit Sesame. Before that, he was a machine learning engineer at Apple, where he worked on autonomous systems, personalized health, and silicon engineering.

    Applications of Modern Survival Modeling with Python(Talk)

     

    Senior Software EngineerQuansight Lab

    Thomas Fan

    Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.

    Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

    Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

    Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

    Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)


    See all our talks and hands-on workshop and training sessions
    See all sessions

    You Will Meet

    • Top speakers and practitioners in Machine Learning and Deep Learning

    • Data Scientists and Data Analysts

    • Decision makers

    • Software Developers focused on Machine Learning and Deep Learning

    • Data Science Innovators

    • CEOs, CTOs, CIOs

    • Industry leaders

    • Core contributors in the fields of Machine Learning and Deep Learning

    • Data Science Enthusiasts

    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 its coming impact on the domains of business, society, healthcare, finance, manufacturing, and more

    Sessions on Machine Learning & Deep Learning Track

    • Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability

    • Talk: Adversarial Attacks on Deep Neural Networks

    • Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow

    • Workshop: Machine Learning for Digital Identity

    • Talk: Adding Context and Cognition to Modern NLP Techniques

    • Training: Good, Fast, Cheap: How to do Data Science with Missing Data

    • Workshop: Open Data Hub workshop on OpenShift

    • Talk: Practical AI solutions within healthcare and biotechnology

    • Training:  Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale

    • Workshop: Reproducible Data Science Using Orbyter

    • Talk: Combining millions of products into one marketplace using computer vision and natural language processing

    • See the schedule!

    ODSC EAST 2022 | April 20th-21st

    Register your interest
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