Course curriculum

  • 1

    ODSC Keynotes

    • The Big Wave of AI at Scale by Luis Vargas, PhD

    • Bridging the Gap Between Data Scientists and Decision Makers by Ken Jee

    • Physics-inspired Learning on Graph by Michael Bronstein, PhD
  • 2

    Demo Talks

    • Run Azure Machine Learning Anywhere in Multi-cloud or on Premises ​by Doris Zhong

    • Introduction to WSL2 for Data Science with Z by HP by Akram Dweikat

    • Graph Embeddings: 5 Ways Your AI Can Learn From Your Connected Data by Nicolas Rouyer

    • The Rapid Evolution of the Canonical Stack for Machine Learning by Lee Baker

    • The Hidden Layers of Tech Behind Successful Data Labeling by Glen Ford

    • Is Reinforcement Learning the Right Tool for Your Problem by Prof. Pulkit Agrawal
  • 3

    ODSC Talks

    • Flawed Machine Learning Security: The Top Security Flaws in the ML Lifecycle (and how to avoid them) by Alejandro Saucedo

    • Data Science Innovation with Z by HP Workstations and Software Stack by Bradley Franko, Hunter Kempf

    • Digital Twins_ Not All Digital Twins are Identical by Dr. Anand Srinivasa Rao

    • Social Biases in Text Representations and their Mitigation by Danushka Bollegala, PhD

    • Building Machine Learning Systems for the Era of Data-centric Ai by Ce Zhang, PhD

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

    • Vector Search for Data Scientists by Connor Shorten

    • Scientific Discovery and Unsupervised Disentanglement by Yair Weiss, PhD

    • Ethnicity, Equity, and AI by Sara Khalid

    • Human-Friendly, Production-Ready Data Science with Metaflow by Ville Tuulos

    • ELLIS Alicante Unit Foundation _ Data-Pop Alliance - Data Science Against COVID-19 by Nuria Oliver, PhD

    • Eagleeye: Data Pipeline for Anomaly Detection in Cyber Security by Tuhin Sharma

    • Scaling Machine Learning with Data Mesh by Shawn Kyzer

    • Computer Perception Challenges in Drone Applications Using Quality Data Annotation by Keith McCormick

    • Model Based Deep Learning with Applications to Imaging by Yonina Eldar, PhD

    • What's new in Apache Airflow 2 by Kaxil Naik

    • Data Science, Meet Data Mesh: What We Can Learn from Bioinformatics about the Power of Standardization in Distributed Systems by Dan Sullivan, PhD

    • A Systematic Approach for Building Full-Spectrum Model Monitoring by Mihir Mathur

    • Leaner and Greener AI with Quantization in PyTorch by Suraj Subramanian
  • 4

    Hands on Workshops & Tutorials

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

    • Full-stack Machine Learning for Data Scientists by Hugo Bowne-Anderson, PhD

    • Prediction with Missing Values by Gael Varoquaux, PhD

    • StructureBoost: Gradient Boosting with Categorical Structure by Brian Lucena, PhD

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

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

    • Continual Visual Learning by Karteek Alahari, PhD

    • Visually Inspecting Data Profiles for Data Distribution Shifts by Felipe de Pontes, Bernease Herman

    • Time-Series in Python - Preprocessing and Machine Learning by Ben Auffarth, PhD

    • Applying Interactive Weak Supervision to NLP Tasks by Shayan Mohanty

    • GANs N' Roses: Understanding Generative Models by Daniel Voigt Godoy

    • Transfer Learning in NLP with Transformers by Jayeeta Putatunda

    • Deep Reinforcement Learning for Asset Allocation in US Equities by Sonam Srivastava

    • Sentiment Analysis Tricks with Keras, spaCy and Transformers by Duygu Altinok, PhD
  • 5

    Extra Events

    • Women in Ignite by Flora Tasse, PhD, Nollie Maoto, Laia Subirats, PhD