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Top Machine and Deep Learning Sessions Coming to ODSC East 2021 Top Machine and Deep Learning Sessions Coming to ODSC East 2021
Machine learning and deep learning are cornerstones of the data science industry. As such, not only is it important to have... Top Machine and Deep Learning Sessions Coming to ODSC East 2021

Machine learning and deep learning are cornerstones of the data science industry. As such, not only is it important to have a firm foundation in these topics, it is essential to keep up-to-date on the latest advancements. At ODSC East, there will be several training sessions, workshops, and talks on machine learning and deep learning from some of the best and brightest minds in data science and AI. Here are just a few ODSC East machine learning and deep learning sessions we are excited about.

AI Explainability in The Real World
Violeta Misheva, PhD Data Scientist | ABN AMRO Bank N.V.

In this talk, you will examine explainability through the lens of the financial domain and discuss some pros and cons of explaining machine learning models. You will also explore where explainability fits in the machine learning development cycle and what different stakeholders may need from it. 

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples
Dr. Kirk Borne | Principal Data Scientist | Booz Allen Hamilton

This workshop will address the cold-start problem in machine learning. During this session you will cover several machine learning modeling examples, suggested solutions to their cold-start challenges, and related concepts, including the objective function, genetic algorithms, backpropagation, gradient descent, and meta-learning.

Atypical Applications of Typical Machine Learning Algorithms
Dr. Kirk Borne | Principal Data Scientist | Booz Allen Hamilton

This workshop will explore the ways that algorithms developed for specific applications can be used in unexpected ways. These exercises serve to demonstrate how data scientists can create even more value, beyond that which is expected, from our data sets and our algorithmic talents.

Probabilistic Programming Bayesian Inference with Python
Lara Kattan | Data Science Manager  | EY

In this session, you will learn how to utilize PP to do Bayesian inference and solve problems that aren’t otherwise tractable with classical methods.

Machine Learning and AI in 2021: Recent Trends, Technologies, and Challenges
Sebastian Raschka, PhD | Professor, Researcher, Author of ‘Python Machine Learning’ | University of Wisconsin-Madison

In this talk, you will explore recent research and technology advances and trends of the last year(s) concerning GPU-accelerated machine learning and deep learning and focusing on the most profound hardware and software paradigms that have enabled it.

Snakemake: A Python Pipeline Toolbox
Laura A. Seaman, PhD | Machine Intelligence Scientist | Draper

During this session you will learn how to use Snakemake,a workflow management system that uses sets of rules to define steps in the analysis process and it integrates smoothly with server, cluster, or cloud environments to allow easy scaling, to create a scalable and reproducible data analysis pipeline. 

XAI – Explanation in AI: From Machine Learning to Knowledge Representation & Reasoning and Beyond
Freddy Lecue, PhD | Chief AI Scientist | Research Associate | Thales | INRIA

This presentation will describe progress to date on XAI, or a set of tools for explaining AI systems of any kind, by reviewing its approaches, motivation, best practices, industrial applications, and limitations.

Echo State Networks for Time-Series Data
Teal Guidici, PhD | Machine Intelligence Scientist | Draper

In this session, you will be introduced to the theory, key parameters in implementation, and practical considerations of Echo State Networks, a type of recurrent neural network. You will leave this session a basic understanding of Echo State Networks, how to use the EchoTorch python module, the impact key parameters have on algorithm performance and potential application areas.

A New Measurements-Based Approach to Machine Learning
Dr. Gerald Friedland | CTO & Co-Founder | Adjunct Professor, Electrical Engineering and Computer Sciences Brainome | University of California, Berkeley

In this talk, you will examine a different approach to supervised machine learning that is rooted in measurements. You will explore how this new approach can be used to solve difficult machine learning problems, many of which have previously been out of reach, the fundamentals of how measurements-based machine learning works, and how the approach can be applied to solve real-world problems in bioinformatics and other fields.

Linear Algebra, Calculus, and Probability: The Math ML Experts Master
Dr. Jon Krohn | Chief Data Scientist, Author of ‘Deep Learning Illustrated’ | Untapt

In this session you will use hands-on code demos to examine why linear algebra, calculus, and probability are essential in machine learning and necessary to train innovative models or deploy them to run performantly in production.

Register now and see all ODSC East Machine Learning and Deep Learning sessions

These are just a few of the training sessions, workshops, and talks on machine learning and deep learning that will be featured at ODSC East in just a few days. You can find a complete list here. And don’t forget to register before it’s too late!

ODSC Community

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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