Course curriculum

  • 1

    Keynotes

    • Data Science to Fight Against COVID-19 by Nuria Oliver, PhD

    • How Turkcell Democratizes Data Science and Accelerates AI Innovation to Transform Customer Experience by İnanç Çakıroğlu and Abhinav Joshi

    • Bayesian Modeling without the Math by Thomas Wiecki, PhD

    • Parsing Engineering Diagrams for Industrial AI Applications using Deep Learning and Graph Search

    • Accountable AI in Europe: Are We Ready for the Artificial Intelligence Act? by Sandra Wachter, PhD

    • Fostering AI Innovations Through Open Source Projects
  • 2

    ODSC Talks

    • What's the Deal with Managed Services and Model Delivery?

    • Error Analysis for Accelerating Responsible Machine Learning

    • Benefits of Convolutional Neural Network for Healthcare Shortage Classification in Underserved Community

    • The Role of Data Virtualization in AI/ML Projects - A Demonstration

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

    • The Colours of Cleaning

    • The Missing Link: How AI Can Help Create a Safer Society and Better Businesses

    • Deconstructing MLOps

    • Evolution of Efficient and Robust AutoML Systems by Frank Hutter, PhD

    • Industrial Artificial Intelligence – From Automated Process to Cognitive Analytics by Diego Galar, PhD

    • Most Popular Reasons ML Projects Fail and How to Avoid Them by Natasha Montagu

    • Incentives for Sociality: Intrinsic and Extrinsic Motivations of Social Dilemmas in Multi-Agent Systems by Edgar A Duenez-Guzman, PhD

    • Interpretable Machine Learning to Model Drug Perturbations in Single Cell Genomics by Dr. Fabian Theis

    • Finding that Needle! Modern Approaches to Fraud and Anomaly Detection by Aric LaBarr, PhD

    • Production Machine Learning Monitoring: Principles, Patterns and Techniques by Alejandro Saucedo

    • Meta-Learning: Learning to learn by Nisha Muktewar

    • Learning from Failure by Kamila Hankiewicz, Ivana Pejeva, Bogumił Kamiński

    • Smart City Data Pipeline, an Edge to Core Data Story by Guillaume Moutier

    • Exploring Modern and Secure Operations of Kubernetes Clusters on the Edge by Lucas Käldström

    • Building and Managing Advanced Analytics & AI Teams by John K. Thompson

    • What Do I See in This Data? Visual Tools to Enhance Data Understanding by Max Novelli

    • Model Governance: A Checklist for Getting AI Safely to Production by David Talby, PhD

    • Overcoming the Cold Start Problem: How to Make New Tasks Tractable by Azin Asgarian, Franziska Kirschner, PhD

    • Fairness in Medical Algorithms: Threats and Opportunities by Judy Gichoya

    • Women Ignite by Zakaria Tolba, Sharmistha Chatterjee, Cvetanka Eftimoska, Nutsa Abazadze, Hajar Khizou

    • Machine Learning for Planetary Health: Challenges, Opportunities, and Doing Our Bit by Sara Khalid

    • Exploring the Rimworld of Sound Space Using Generative Adversarial Networks by Laurens Koppenol, David Isaacs Paternostro

    • Building Real-Time ML Pipelines the Easy Way by Yaron Haviv

  • 3

    Career Mentor Talks

    • Keep it Simple: How to Talk to Executives in an Effective Way by Daniela Petruzalek

    • How I Became a Data Science Consultant and Other Stories by James Keirstead, PhD

    • Build your Own Job by Jack Raifer

    • Academia, Startups, and Enterprise: A Cross-Analysis of Work and Goals by Dan Shiebler
  • 4

    Demo Talks

    • ML Data Challenges and Market Insights

    • Data Acquisition and Governance: Considerations for Success

    • The Next Evolution of PyTorch Performance Debugging

    • Integrating Data Science and Application Development

    • Feature Stores: Your MLOps Competitive Advantage by Adi Hirschtein

    • Comprehending ClearML and MLOps - Enabling the New A-Z

    • ML Observability: A Critical Piece in Making Models Work in the Real World by Aparna Dhinakaran

    • Augmented Intelligence: How Machine Learning + Human Analytics Catches Criminals by Danny Leybzon

    • Why is Data Virtualization a Data Scientist’s Best Friend? by Robin Tandon

    • Intro to Seldon Deploy: Deployment, Management and Monitoring of ML Models in Production by Tom Farrand

    • Neo4j Demo: Graph-Native Machine Learning and Predictions

    • Computer Vision: Seeing the Value for the Pixels - Lessons Learned from Real World Applications by Federica Citterio

    • Using News Analytics & Graphs to Inform Investment Decisions by Peter Hafez
  • 5

    ODSC Workshops

    • Hands-on RL in Finance: Playing Atari vs Playing Markets by Alex Honchar

    • Part 1- Adversarial Attacks and Defence in Computer Vision 101

    • Part 2 - Adversarial Attacks and Defence in Computer Vision 101

    • Part 3 - Adversarial Attacks and Defence in Computer Vision 101

    • PyTorch 101: Building a Model Step-by-Step by Daniel Voigt Godoy

    • Explainable Artificial Intelligence Explained by Karol Przystalski

    • Responsible Data Science Using Bias-Dashboards

    • Machine Learning For Remote Sensing Based Landcover Change Detection by Minerva Singh, PhD

    • Build an ML Pipeline with Airflow and Kubernetes

    • MLOps Will Change Machine Learning

    • Closing the Production Gap with MLOps by Asger Pedersen, Qian Zhao PhD, Pavel Ustinov PhD

    • Generative AI to Create Image and Deepfake Video

    • Rule Induction and Reasoning in Knowledge Graphs by Daria Stepanova, PhD

    • Introduction To Face Processing With Computer Vision by Gabriel Bianconi

    • Mastering Gradient Boosting with CatBoost by Stanislav Kirillov

    • MLOps Orchestration: Your Highway to Accelerating Deployment of AI by Yaron Haviv

    • Federated Learning from Scratch to Production with Scaleout by Daniel Zakrisson

    • Introduction to Transformers for NLP: Where We Are and How We Got Here by Olga Petrova, PhD

    • An Introduction to Machine Learning in Quantitative Finance by Dr. Hao Ni

    • Advances in Conversational AI and NLP through Large Scale Language Models such as GPT-3 by Chandra Khatri

    • Machine Learning for Economics and Finance in TensorFlow 2 by Isaiah Hull

    • Building Data Lakes by Ivana Pejeva

    • Classification Algorithms using Python and Scikit-Learn by Yamini Rao

    • Federated Learning_ Practice and Modern Algorithms by Mikhail Yurochkin, PhD, Chaitanya Kumar, Dr. Laura Wynter