Hands-on Tutorials

We have a fantastic lineup of hands-on tutorials to be held in conjunction with KDD 2020. Check back as we get closer to the conference for more detailed program information.

  • From Zero to AI Hero with Automated Machine Learning

    Presenters: Aniththa Umamahesan: Microsoft; Deepak Mukunthu Iyappan Babu: Microsoft; Cesar De la Torre Lorente: Microsoft; Anup Shirgaonkar: Microsoft

  • Put Deep Learning to work: Accelerate Deep Learning through AWS EC2 and ML Services

    Presenters: Wenming Ye: Amazon Web Services; Rachel Hu: Amazon

  • Building Forecasting Solutions Using Open-Source and Azure Machine Learning

    Presenters: Vanja Paunic: Microsoft; Chenhui Hu: Microsoft

  • How to Calibrate your Neural Network Classifier: Getting true probabilities from a classification model

    Presenters: Natalia Culakova: nPlan; Daniel Murphy: nPlan

  • Neural Structured Learning: Training neural networks with structured signals

    Presenters: Arjun Gopalan: Google; Da-Cheng Juan: Google; Cesar Magalhaes: Google; Allan Heydon: Google; Chun-Sung Ferng: Google; Philip Pham: Google; George Yu: Google; Chun-Ta Lu: Google

  • Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS

    Presenters: Bartley Richardson: NVIDIA; Brad Rees: NVIDIA; David Bader: New Jersey Institute of Technology; Keith Kraus: NVIDIA; Tom Drabas: Microsoft; Joshua Patterson: NVIDIA

  • DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters

    Presenters: Jing Zhao: Microsoft Bing; Yuxiong He: Microsoft; Samyam Rajbhandari: Microsoft; Hongzhi Li: Microsoft Research; Jeff Rasley: Microsoft

  • Robust Deep Learning Methods for Anomaly Detection

    Presenters: RAGHAVENDRA CHALAPATHY: Data61 CSIRO; Khoa Nguyen: Data61-CSIRO; Sanjay Chawla: QCRI

  • Text Summarization Recipes: Overview, Applications and Best Practices

    Presenters: Hong Lu: Microsoft; Emmanuel Awa: Microsoft; Said Bleik: Microsoft

  • Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data

    Presenters: Jonas Mueller: Amazon Web Services; Xingjian Shi: Amazon Web Services; Alexander Smola: Amazon

  • Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges

    Presenters: Rich Caruana: Microsoft Research; Scott Lundberg: Microsoft Research; Marco Ribeiro: Microsoft Research; Harsha Nori: Microsoft; Samuel Jenkins: Microsoft; Mehrnoosh Sameki: Microsoft

  • Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial

    Presenters: Pedro Saleiro: University of Chicago; Kit Rodolfa: Carnegie Mellon University; Rayid Ghani: Carnegie Mellon University

  • Deep Learning for Search and Recommender Systems in Practice

    Presenters: Jun Jia: LinkedIn Corporation; Bo Long: LinkedIn Corporation; Huiji Gao: LinkedIn Corporation; Mingzhou Zhou: LinkedIn Corporation

  • Computer Vision: Deep dive into object segmentation approaches

    Presenters: Yuanbo Wang: The Home Depot; Osama Sakhi: The Home Depot; Matthew S Hagen: Home Depot; Estelle Afshar: The Home Depot; Ala Eddine Ayadi: LVMH

  • In Search for A Cure: Recommendation with Knowledge Graph on CORD-19

    Presenters: Le Zhang: Microsoft; Le Zhang: Microsoft; Zhihong Shen: Microsoft Research Redmond; Jianxun Lian: MSRA; Chieh-han Wu: Microsoft Research Redmond; Miguel Fierro: Microsoft; Andreas Argyriou: Microsoft

  • Scalable Graph Neural Networks with Deep Graph Library

    Presenters: Da Zheng: Amazon; George Karypis: Amazon; Zheng Zhang: Amazon; Minjie Wang: New York University; Quan Gan: Amazon

  • Introduction to computer vision and realtime deep learning-based object detection

    Presenters: JAMES G SHANAHAN: Church and Duncan Group; Liang Dai: Facebook

  • Building Recommender Systems with PyTorch

    Presenters: Dheevatsa Mudigere: Facebook; Maxim Naumov: Facebook, Inc.; Joe Spisak: Facebook; Geeta Chauhan: Facebook

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