The Claude Shannon Way of Modeling

Shannon's Communication Theory and its Applications in Data Science

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Gain 10x gains in data science team efficiency

There is a tendency amongst data scientists to immediately dive into fitting a complex model on a new dataset without taking time to understand the data or whether or not the model can even answer the business question the analysis is meant to answer.

For many, the craft of data science and machine learning boils to importing from a machine learning bag of tricks,

ML Bag of Tricks

and tweaking nuances of this training workflow. With enough practice, a practitioner gets paid handsomely to repeatedly build, debug, and rebuild this curve-fitting workflow across various problems.

But as the market for glorified curve-fitters saturates, how will you and your team add value?

After completing this workshop, practitioners will have had a complete refactoring of their mental frameworks for modeling.

Specifically, they will have

  1. Seen how to iteratively build a generative machine learning model from scratch using probabilistic programming.
  2. Used deep learning tools without buying into deep learning hype.
  3. Gone beyond accuracy and connect model performance directly to a business's key performance indicators.
  4. The ability to talk about the inductive bias of a model using language from the domain they are modeling.
  5. Applied mental models from Bayesian decision theory, information theory, Shannon's communication theory, and dynamic systems to model building.

Your Instructor

Robert Osazuwa Ness
Robert Osazuwa Ness

Robert is currently a research scientist at Microsoft Research and faculty at the Northeastern University department of computer science. He has worked in industry as a research engineer and data scientist building production quality systems for Bayesian decision-making under uncertainty.

Robert didn't start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.

Robert attained his Ph. D. in mathematical statistics from Purdue University. He has published in top tier journals and venues on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems.

The ML community is in a mental rut. It's time for you to opt-out.

Break free from trend-following with AltDeep's radical approach to machine learning.

Those who complete this course break the mold of engineer/researcher/data scientist. They walk away with unconventional causality-based mental models for tackling practical problems in practice and research.

Get started now!