Course curriculum

  • 01

    Machine Learning

    • Keynote: Towards a Blend of Machine Learning & Microeconomics - Michael I. Jordan

      FREE PREVIEW
    • Advanced Methods for Explaining XGBoost Models - Brian Lucena, PhD

    • Building a Portfolio for Applied Data Science Roles - Ben Weber, PhD

    • Building an Industry classifier with the latest scraping, NLP and deployment tools - Ido Shlomo

    • Causal Inference for Data Science - Vinod Bakthavachalam

    • Composable Machine Learning - Eric Xing, PhD

    • EMI: Embed, Measure and Iterate - Mayank Kejriwal, PhD

    • Enterprise Grade Data Labeling - Design Your Ground Truth to Scale in Production - Jai Natarajan

    • Explainable Machine Learning - Eitan Anzenberg

    • Healthcare NLP with a Doctor's Bag of Notes - Andrew Long, PhD

    • How to Build a Recommendation Engine That Isn’t Movielens - Max Humber

    • Incorporating Intent Propensities in Personalized Next Best Action Recommendation - Kexin Xie

    • Learning From Limited Data - Shanghang Zhang, PhD

    • Looking from Above: Object Detection and Other Computer Vision Tasks on Satellite Imagery - Xiaoyong Zhu, Siyu Yang

    • Machine Learning (ML) on Devices: Beyond the Hype - Divya Jain

    • Machine Learning for User Conversion and Global Marketplace Optimization at Upwork (Part 1: Optimize User Level Growth) - Thanh Tran, PhD

    • Machine Learning Interpretability Toolkit - Mehrnoosh Sameki, PhD

    • Missing Data in Supervised Machine Learning - Andras Zsom, PhD

    • Optuna: A Define-by-Run Hyperparameter Optimization Framework - Crissman Loomis

    • Principled Methods for Analyzing Weight Matrices of Modern Production Quality Neural Networks - Michael Mahoney, PhD Charles Martin, PhD

    • Product Search in E-Commerce: What to Optimize? - Liang Wu, PhD

    • Real-ish Time Predictive Analytics with Spark Structured Streaming - Scott J Haines

    • Responsible AI Requires Context and Connections - Amy E. Hodler

    • The Expense of Poorly Labeled Data. What Causes ML Models to Break? - Nikhil Kumar

    • When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution - Gregory Valiant, PhD

  • 02

    Deep Learning

    • 10 Things You Didn't Know About TensorFlow in Production - Chris Fregly

    • AI and Security: Lessons, Challenges and Future Directions - Dawn Xiaodong Song, PhD

    • An Inconvenient Truth about Artificial Intelligence - Yaron Singer, PhD

    • Combining Word Embeddings with Knowledge Engineering - Sanjana Ramprasad

    • Combining Word Embeddings with Knowledge Engineering - Sanjana Ramprasad

    • Community-Specific AI: Building Solutions for Any Audience - Jonathan Purnell, PhD Yacov Salomon, PhD

    • Deciphering the Black Box: Latest Tools and Techniques for Interpretability - Rajiv Shah, PhD

    • Enabling Powerful NLP Pipelines with Transfer Learning - Lars Hulstaert

    • Lessons Learned Deploying a Deep Learning Visual Search Service at Scale - Scott Cronin, PhD

    • Named Entity Recognition At Scale With Deep Learning - Sijun He

    • Neural Networks from Scratch with Pytorch - Brad Heintz

    • Planetary Scale Location-based Insights - Gopal Erinjippurath

    • Practical Deep Learning for Images, Sensor and Text - Renee Qian

    • Project GaitNet: Ushering in The ImageNet Moment for Human Gait kinematics - Vinay Prabhu, PhD

    • Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems - Veysel Kocaman, PhD

    • Tutorial on Deep Reinforcement Learning: Part 1 - Pieter Abbeel, PhD

    • Tutorial on Deep Reinforcement Learning: Part 2 - Pieter Abbeel, PhD

    • Tutorial on Deep Reinforcement Learning: Part 3 - Pieter Abbeel, PhD

    • Validate and Monitor Your AI and Machine Learning Models - Olivier Blais

    • World-scale Deep Learning for Automated Driving - Sudeep Pillai, PhD

  • 03

    Data Visualization

    • Declarative Data Visualization with Vega-Lite & Altair - Kanit Wongsuphasawat, PhD Dominik Moritz

    • Mapping Geographic Data in R - Joy Payton

    • Modernizing Your Data Visualization Strategy - Gary Young

    • Sports Analytics - Leveraging Raw GPS Data for Optimizing Soccer Players' Performance - Christopher Connelly

    • The Power of Visualization: Best Practices for Effective Visualizations - Cathy Tanimura

  • 04

    DevOps & Management

    • Active Learning to Combat Fraud at Scale - Nitesh Kumar, PhD

    • Chaos and Pain in Machine Learning, and the ‘DevOps for ML Manifesto’ - Nick Ball, PhD

    • Faster Data Science with the RapidFile Toolkit — Harnessing the Power of Parallel - Miroslav Klivansky, PhD

    • Getting Your RoAI - Return on AI - Now, Not Months Down the Road - Pedro Alves, PhD

    • Integrating Elasticsearch with Analytics Workflows - Stephanie Kirmer

    • Machine Learning Workflows For Software Engineers - Will Benton Sophie Watson

    • MLOps: ML Engineering Best Practices from the Trenches - Sourav Dey, PhD Alex Ng

    • Productized Automated Model Building: How to Go From Data to Deployment with Neuroevolution - Keith Moore -

    • Without Human Expertise, Artificial Intelligence is Pretty Dumb - Rahul Singhal

  • 05

    AI for Engineers

    • Deploying AI for Near Real-Time Engineering Decisions - Heather Gorr, PhD

    • diff software_dev software_dev*ai - Jana Eggers

    • Mining “Concept Embeddings” from Open-Source Data to Classify Previously Unseen Log Messages - David Nellinger Adamson, PhD

    • Pomegranate: Fast and Flexible Probabilistic Modeling in Python - Jacob Schreiber

    • The Power of Workflows - Cliff Clive

  • 06

    Open Source

    • Building Modern ML/AI Pipelines with the Latest Open Source Technologies - Chris Fregly

    • Creating an Extensible Big Data Platform to Serve Data Scientists and Analysts - 100s of PetaBytes with Realtime Access - Reza Shiftehfar, PhD

    • How We Ran a Dog Image Generation/GAN Competition on Kaggle - Wendy Chih-wen Kan, PhD

    • Simplified Data Preparation for Machine Learning in Hybrid and Multi Clouds - Bin Fan, PhD

  • 07

    Research Frontiers

    • Building The Future: Deep-Learning for Autonomous Vehicles - Chen Wu, PhD

    • Computer Vision for Omnichannel Retail: Intelligent Analysis and Selection of Product Images at Scale - Abon Chaudhuri, PhD

    • Data Harmonization for Generalizable Deep Learning Models: from Theory to Hands-on Tutorial - Gerald Quon, PhD Nelson Johansen

    • Imagination Inspired Vision - Mohamed Elhoseiny, PhD

    • Opening the Pod Bay Doors: Building Intelligent Agents That Can Interpret, Generate and Learn from Natural Language - Jacob Andreas, PhD

    • Quantamental Factor Investing Using Alternative Data and Machine Learning - Arun Verma, PhD

    • The Robustness Problem - Justin Gilmer, PhD

  • 08

    AI for Innovation (Accelerate AI Summit)

    • Accelerating AI-driven Innovation in Your Enterprise - Pallav Agrawal

    • Ai in Healthcare: the State of Adoption - Alex Ermolaev

    • AI in Medicine: Avoiding Hype and False Conclusions - Michael Zalis MD

    • An Introduction to AI's Impact in the Life Sciences - Mark DePristo

    • Designing a User-centric AI Product - Katie Malone, PhD Annie Darmofal

    • Robots Learning Dexterity - Peter Welinder, PhD

      FREE PREVIEW
    • Scaling Computer Vision in the Cloud and AI Chips - Reza Zadeh, PhD

    • The Anatomy of a Payment: Dissecting Data Science - Jennifer Xia

  • 09

    AI for Management (Accelerate AI Summit)

    • Keynote: Data Science is the Discipline of Making Data Useful - Cassie Kozyrkov

      FREE PREVIEW
    • Bringing AI Out of the Lab and Into Production - Irina Farooq

    • Building a Center of Excellence for Data Science - Michael Xiao

    • Building AI Products: Delivery Vs Discovery - Charles Martin, PhD

    • Dominant Pattern Detection in Undirected Graphs - Henry Chen, PhD Vidhya Raman Jingru Zhou, PhD

    • Establishing a Data and Analytics Organization - Shanthi Iyer

    • From R&D to ROI: Realize Value by Operationalizing Machine Learning - Diego Oppenheimer

    • From Silos to Platform: Building Twitter's Feature Marketplace - Wolfram Arnold, PhD

    • On AI ROI: the Questions You Need to Be Asking - Kerstin Frailey

    • Weaponizing Distraction: How to Use Analytics on Call Rotations for Improving Team Focus, Onboarding New Employees, and Making Space for Career Growth - Katie Bauer

    • Why We Should Hire More Analysts for Data Science Teams - Benn Stancil

  • 10

    AI for Expertise (Accelerate AI Summit)

    • Challenges of Digital Transformation and AI - Rashed Haq, PhD

    • Data-driven Approaches to Forecasting - Javed Ahmed, PhD

    • Deployment of Strategic AI in the Enterprise - Dr. Fernando Nunez-Mendoza

    • Developing Machine Learning-driven Customer-facing Product Features - Marsal Gavalda, PhD

    • Enterprise Adoption of Reinforcement Learning - Dr. Ganapathi Pulipaka

    • Harnessing AI: Data Evangelism Must Be Data-driven - Jennifer Redmon

    • Natural Language Processing: Deciphering the Message Within the Message – Stock Selection Insights Using Corporate Earnings Calls - Frank Zhao

    • Race Your Facts: Making AI Work for Enterprises - Rama Akkiraju

    • Scaling 200b+ Pins Using a Mix of Machine Learning and Human Curation - Chuck Rosenberg, PhD

    • Sources of Bias: Strategies for Tackling Inherent Bias in Ai - Harry Glaser

    • The Last Frontier of Machine Learning - Data Wrangling - Alex Holub, PhD

    • Transaction Data Enrichment – an Opportunity for Business Growth and Risk Mitigation - Pramod Singh, PhD