Boston | April 13th – April 17th, 2020

Machine Learning & Deep Learning Track

Learn the latest models, advancements and trends from the top practitioners behind two of data science’s hottest topics

Comprising of multiple tracks this focus area is where leading experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.

Attend talks, tutorials and workshops and hear from the creators and top practitioners as they demonstrate and teach the latest models and trends in Machine Learning and Deep Learning to solve problems in business and society. Some of the topics you’ll learn include:

  • Machine Learning

  • Deep Learning

  • Deep Reinforcement Learning

  • Neural Networks

  • LSTM, CNNs, RNNs, & GANs,

  • Computer Vision

  • Pattern Recognition

  • Tensorflow

  • Scikit-learn

  • Keras

  • Caffe 2

  • PyTorch

  • Theano

  • Apache Spark & MlLib

  • and many more…

  • Federated Learning

  • Transfer Learning

  • Autonomous Machines

  • MLOps and Kubeflow

  • Recommendation Systems

  • Never Ending Learning for ML

  • Causal Inference

Some of Current ML & DL Speakers


Click Here For Full Lineup
2020 Speakers

Sample Talk, Workshop, and Training Sessions

Machine Learning & Deep Learning Sessions
Advanced Machine Learning: Pipelines and Evaluation Metrics

Training | Machine Learning | ML for Programmers | Advanced

 

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines and advanced model evaluation. Model evaluation is an underappreciated aspect of machine learning, but using the right metric to measure success is critical. Practitioners are often faced with imbalanced classification tasks, where accuracy can be uninformative or misleading. We will discuss other metrics, when to use them, and how to compute them with scikit-learn. We will also look into how to build processing pipelines using scikit-learn, to chain multiple preprocessing techniques together with supervised models, and how to tune complex pipelines…more details

Advanced Machine Learning: Pipelines and Evaluation Metrics image
Andreas Mueller, PhD
Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute
Advanced Machine Learning with scikit-learn: Imbalanced Classification and Text Data

Training | Machine Learning | ML for Programmers | Advanced

 

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some advanced topics in using scikit-learn and how to build your own models or feature extraction methods that are compatible with scikit-learn. We will also discuss different approaches to feature selection and resampling methods for imbalanced data. Finally, we’ll discuss how to do the classification of text data using the bag-of-words model and its variants…more details

Advanced Machine Learning with scikit-learn: Imbalanced Classification and Text Data image
Andreas Mueller, PhD
Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute
From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime

Workshop | ML for Programmers | Deep Learning | Intermediate

 

Powerful Machine Learning models trained using various frameworks such as scikit-learn, PyTorch, TensorFlow, Keras, and others can often be challenging to deploy, maintain, and performantly operationalize for latency-sensitive customer scenarios. Using the standard Open Neural Network Exchange (ONNX) model format and the open source cross-platform ONNX Runtime inference engine, these models can be scalably deployed to cloud solutions on Azure as well as local devices ranging from Windows, Mac, and Linux to various IoT hardware. Once converted to the interoperable ONNX format, the same model can be served using the cross-platform ONNX Runtime inference engine across a wide variety of technology stacks to provide maximum flexibility and reduce deployment friction.

In this workshop, we will demonstrate the versatility and power of ONNX and ONNX Runtime by converting a traditional ML scikit-learnpipeline to ONNX, followed by exporting a PyTorch-trained Deep Neural Network model to ONNX. These models will then be deployed to Azure as a cloud service using Azure Machine Learning services, and to Windows or Mac devices for on-device inferencingmore details

From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime image
Faith Xu
Senior Program Manager | Microsoft
From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime image
Prabhat Roy
Data Scientist | Microsoft
Modern and Old Reinforcement Learning

Full-Day Training | Deep Learning | Machine Learning | Beginner-Intermediate

 

In this workshop we will explore Reinforcement Learning, starting from its fundamentals and ending creating our own algorithms. We will use OpenAI gym to try our RL algorithms. OpenAI is a non profit organization that want committed to open source all their research on Artificial Intelligence. To foster innovation OpenAI created a virtual environment, OpenAi gym, where it’s easy to test Reinforcement Learning algorithms. We then will also explore other RL frameworks and more complex concepts like Policy gradients methods and Deep Reinforcement learning, which recently changed the field of Reinforcement Learning. In particular, we will see Actor-Critic models and Proximal Policy Optimizations that allowed OpenAI to beat some of the best Dota players…more details

Modern and Old Reinforcement Learning image
Leonardo De Marchi
Head of Data Science and Analytics | Badoo (now MagicLab, which owns several apps)
Introduction to Deep Learning & Neural Networks II: Practice

Training | Deep Learning | Intermediate-Advanced

 

In this session, we will focus on the application and practice of building a multi-layer neural network in Python code. We will walk through a lightweight neural network framework and discuss how the concepts from the morning’s session were reduced to code, including optimization, regularizerization, and backpropagation. We will step through a concrete example of building an autoencoder for image compression. Participants will have the option to download and run all of the neural network code on their own computers. No GPU required. Participants are also welcome to observe and absorb…more details

Introduction to Deep Learning & Neural Networks II: Practice image
Brandon Rohrer, PhD
Principal Data Scientist | iRobot
Introduction to Deep Learning & Neural Networks I: Concepts

Training | Deep Learning | Kick-starter | Beginner

 

This session will focus on the foundational concepts of neural networks and deep learning. Concrete examples will be used to illustrate abstract concepts and methods. Participating in this workshop you will gain an intuition for the principles underlying modern neural networks including multilayer perceptrons, autoencoders, convolutional neural networks, and recurrent neural networks including long short-term memory. No prior experience with neural networks or machine learning required…more details

Introduction to Deep Learning & Neural Networks I: Concepts image
Brandon Rohrer, PhD
Principal Data Scientist | iRobot
Machine Learning for Trading

Training | Machine Learning | Open-source | Beginner-Intermediate

 

The rapid progress in machine learning (ML) and the massive increase in the availability and diversity of data has enabled novel approaches to quantitative investment. It has also increased the demand for the application of data science to develop both discretionary and algorithmic trading strategies.
In this workshop, we will cover popular use cases for ML in the investment industry, and how data science and ML fit into the workflow of developing a trading and investment strategy from the identification and combination of alpha factors to strategy backtesting and asset allocation.
The workshop uses Python and various standard data science and machine learning libraries like pandas, scikit-learn, gensim, spaCy as well as TensorFlow and Keras. The code examples will be presented using jupyter notebooks and are based on my book ‘Machine Learning for Algorithmic Trading’…more details

Machine Learning for Trading image
Stefan Jansen
Founder and Lead Data Scientist | Applied AI
Deep Learning (with TensorFlow 2)

Training | Deep Learning | Machine Learning | Beginner-Intermediate

 

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing. This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2, the major, cutting-edge revision of the world’s most popular Deep Learning library. To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, the essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on Python code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models…more details

Deep Learning (with TensorFlow 2) image
Dr. Jon Krohn
Chief Data Scientist, Author of Deep Learning Illustrated | Untapt
Managing the Complete Machine Learning Lifecycle with MLflow

Training | MLOps & Data Engineering | Machine Learning | Beginner-Intermediate

 

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To solve these challenges, MLflow, an open-source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size…more details

Managing the Complete Machine Learning Lifecycle with MLflow image
Jules Damji
Apache Spark Community & Developer Advocate | Databricks
Adapting Machine Learning Algorithms to Novel Use Cases

Training | Machine Learning | Intermediate

 

How can an idea from an 18th-century Presbyterian minister be used to estimate the mass density function of galaxies across the Universe? How can a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a Formula 1 race from then 1950’s inspire one of the greatest data science use cases for the Internet of Things? How can a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? This workshop will answer these questions, and more, by presenting several examples of one of the key aptitudes of successful data science practice, which is adaptability. In particular, I will present several well-known algorithms (including some that we would not even call “algorithms”) that may have been adopted for specific use cases or applied in specific business domains, and then I will show how each one can be adapted to a novel use case that may be less obvious, perhaps producing significantly surprising results in some other domain. Plus, I will suggest some new opportunities that may come from interesting combinations of data and algorithms. The point of these exercises is to demonstrate how data scientists can create even more value, beyond that which is expected, from our data assets and our algorithmic talents…more details

Adapting Machine Learning Algorithms to Novel Use Cases image
Dr. Kirk Borne
Principal Data Scientist | Booz Allen Hamilton
Solving the Data Scientist’s Dilemma: the Cold-Start Problem with 10+ Machine Learning Examples

Training | Machine Learning | Intermediate

 

We will present at least 10 examples and suggested solutions of cold-start problems (i.e., that move from a bad initial random guess to a good, perhaps optimal, solution), covering a variety of different algorithms and applications, focused primarily on unsupervised learning, but with some supervised learning examples also. We will also introduce related concepts and their importance, including the objective function, genetic algorithms, backpropagation, gradient descent, and meta-learning. Those concepts represent the true keys that unlock performance in a cold-start challenge. Those are the magic ingredients in most of the examples that we will present. At the end of the workshop, you should be empowered, enabled, and emboldened to tackle similar machine learning challenges problems in other domains. After all, data are data, math is math, and good experience is transferable!..more details

Solving the Data Scientist’s Dilemma: the Cold-Start Problem with 10+ Machine Learning Examples image
Dr. Kirk Borne
Principal Data Scientist | Booz Allen Hamilton
Upcoming Session on Machine Learning with R by Jared Lander
Upcoming Session on Machine Learning with R by Jared Lander image
Jared Lander
Chief Data Scientist, Author of R for Everyone, Professor | Lander Analytics, Columbia Business School
Continuous Learning Systems: Building ML Systems That Learn from Their Mistakes

Talk | Deep Learning | Research Frontiers | Beginner-Intermediate

 

Won’t it be great to have ML models that can update their “learning” as and when they make mistake and correction is provided in real time? In this talk, we look at a concrete business use case that warrants such a system. We will take a deep dive to understand the use case and how we went about building a continuously learning system for text classification. The approaches we took, the results we got…more details

Continuous Learning Systems: Building ML Systems That Learn from Their Mistakes image
Anuj Gupta
Senior Leader, Data Science
Understanding the PyTorch Framework with Applications to Deep Learning

Training | Deep Learning | Beginner

 

Over the past couple of years, PyTorch has been increasing in popularity in the Deep Learning community. What was initially a tool used by Deep Learning researchers has been making headway in industry settings. In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner – understanding what is going on “under the hood”, coding the layers of our networks, and implementing backpropagation – to more advanced material on RNNs, CNNs, LSTMs, & GANs. Attendees will leave with a better understanding of the PyTorch framework. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided…more details

Understanding the PyTorch Framework with Applications to Deep Learning image
Robert Alvarez, PhD
Head of Data Science | Podium Education
Upcoming Session by Renowned Machine Learning Researcher and Author Sebastian Raschka
Upcoming Session by Renowned Machine Learning Researcher and Author Sebastian Raschka image
Sebastian Raschka, PhD
Professor, Researcher, Author of 'Python Machine Learning' | University of Wisconsin-Madison
Select date to see events.

See all our talks and hands-on workshop and training sessions
See all sessions

You Will Meet


  • Top speakers and practitioners in Machine Learning and Deep Learning

  • Data Scientists and Data Analysts

  • Decision makers

  • Software Developers focused on Machine Learning and Deep Learning

  • Data Science Innovators

  • CEOs, CTOs, CIOs

  • Industry leaders

  • Core contributors in the field of Machine Learning and Deep Learning

  • Data Science Enthusiasts

Why Attend?


Immerse yourself in talks, tutorials and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends

Expand your network and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career

Meet and connect with the core contributors and top practitioners in the expanding and exciting field of Machine Learning and Deep Learning

Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and its coming impact on the domains of business, society, healthcare, finance, manufacturing, and more

Sessions on Machine Learning & Deep Learning Track

  • Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability

  • Talk: Adversarial Attacks on Deep Neural Networks

  • Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow

  • Workshop: Machine Learning for Digital Identity

  • Talk: Adding Context and Cognition to Modern NLP Techniques

  • Training: Good, Fast, Cheap: How to do Data Science with Missing Data

  • Workshop: Open Data Hub workshop on OpenShift

  • Talk: Practical AI solutions within healthcare and biotechnology

  • Training:  Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale

  • Workshop: Reproducible Data Science Using Orbyter

  • Talk: Combining millions of products into one marketplace using computer vision and natural language processing

  • See the whole schedule!

Sign Up for ODSC EAST 2020 | April 13th – April 17th

Register Now