Course curriculum

  • 1

    ODSC APAC Keynotes

    • Fostering AI Innovations Through Open Source Projects by John Montgomery, Emma Ning and Geeta Chauhan

    • Delivering Advanced Computer Vision Edge AI/ML for Dynamic, Complex and Harsh Work Environments by Dr. Nathan Kirchner

    • AI in Healthcare - Why now by Debashish Banerjee

    • Building Models That Change Our Lives and Society for Good by Ben Taylor, PhD

    • Graph Data Science: What's the Big Deal by Dr. Alicia Frame

    • The Seven Intuitions of a Data Scientist by Dr. Shailesh Kumar

    • Qubole:The Open Data Lake Platform by Nirmal Kumar

  • 2

    ODSC Talks

    • What's Japan Got To Do with World-Class Success in AI and Data by Akira Shibata, PhD

    • The Next Evolution of PyTorch Performance Debugging by Elena Neroslavskaya, Gisle Dankel, Geeta Chauhan

    • ModelOps: Monitoring Performance, Economic Benefit, and Social Responsibility by Jacky Long

    • Data Science Supporting Clinical Decision Making: What, Why, How by Professor Karin Verspoor

    • Graph Development: Tools and IDEs for Designing and Building Graph Solutions on Neo4j by Dr. Tony Wu

    • Predicting Wine Quality with Vertica Machine Learning by Badr Ouali

    • Error Analysis for Accelerating Responsible Machine Learning by Besmira Nushi, PhD, Mehrnoosh Sameki, PhD

    • Data-Centric MLOps 4 Step Process by Marcus Kim

    • Real-Time Machine Learning Models / Applications Monitoring with Imply by Vijay Narayanan

    • Data Science: A Case Study on Customer Churn with Qubole by Rajat Pant

    • Introduction to NLP and Topic Modeling by Sunny Kim

    • Re-Trainable MLOps Systems by Marcus Kim

    • Few Shot Learning for Leprosy by Ramakanteswara Rao Beesetty

    • How to Operationalize Ethics in Data and AI by Angela Kim

    • Python + MPP Database = AI/ML Projects in Production Faster by Paige Roberts

    • Deploying Optimized Deep Learning Pipelines by Adam Gibson

    • Fairness in Natural Language Processing by Tim Baldwin, PhD

    • Enterprise ready ML Model Training on Hybrid Cloud, leveraging Kubernetes by Saurya Das

    • How to Get Started With Deep Reinforcement Learning on a Variety of Use Cases by Maggie Liuzzi

    • Gesture Recognition in the Browser: Training a Computer to Understand Sign Language by Jonathan Chang , Simon Hudson

    • Data Sciences & ML @Qubole by Rajat Pant

    • Relationships Matter: Using Connected Data for Better Machine Learning by Dr. Joshua Yu

    • On Summarization Systems by Dr. Sriparna Saha

    • How to Establish a Successful, Sustainable and Scalable Data Science and AI Capability within an Organisation by Dr. Alex Antic

    • viZio – Content Scoring Engine by Ritesh M Srivastava , Nitin Ranjan Sharma, Soutir Chakraborty ,Sauradeep Debnath

    • AI Singapore's Journey into the World of Federated Learning by Jianshu Weng, PhD , Laurence Liew , Mark Choo, Jin Howe Teo

    • Vision Transformer and its Applications by Rowel Atienza, PhD

    • Advances in Machine Learning for Software Engineering by Aditya Kanade, PhD

    • Journey in Building and Deploying Robust & Executable Data Model for COVID-19 Vaccination (Lessons Learned from Jakarta & Indonesia)
  • 3

    ODSC Tutorials

    • On Human-like Performance Artificial Intelligence through Causal Learning: A Demonstration Using an Atari Game by Seng-Beng Ho, PhD

    • How to do NLP When You Don't Have a Labeled Dataset by Sowmya Vajjala, PhD

    • NLP in Ecommerce by Mathangi Sri

    • Deep Reinforcement Learning based RecSys using Distributed Q-table by Ravi Ranjan

    • Uncover Hidden Business Insights from Unstructured Data by Dr. Lau Cher Han

    • Machine Learning with Spark by A M Aditya

    • Neuro-Symbolic Artificial Intelligence: A Brief Tutorial by Mausam, PhD, Yatin Nandwani

    • Harnessing AI and Knowledge Graphs to Activate Knowledge Discovery in Enterprises by Manprit Singh

    • Model, Task and Data Engineering for NLP by Shafiq Rayhan Joty, PhD