Tutorial: Bayesian Decision Making in The Face of Uncertainty
SkillsCast coming soon.
When you make a decision, in an ideal world, you will have gathered all the pertinent facts and you will weigh them to come to a logical conclusion.
Sadly, you do not live in a perfect world and sometimes you will be forced to make a decision where there is a large degree of uncertainty. In this tutorial, Gary will teach you how to apply the Bayesian principles of machine learning in order to arrive at the best possible decision, by calculating the probability of events happening in an uncertain world. He’ll concentrate on decisions like, should you bet on Tiger Woods to win the Open? Is the mortgage rate likely to go up or down in the next quarter, and should you learn Go, or Rust?
Tutorial Prerequisites
The labs can be completed using either Excel or Python notebook, either via Anaconda installed locally, or a cloud based service such as Azure Notebooks. Attendees should come with/install their preference.
YOU MAY ALSO LIKE:
- Data Science for Fun and Profit (SkillsCast recorded in July 2016)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Fast Track to F# with Tomas Petricek (in London on 31st October - 1st November 2019)
- F# eXchange 2020 (in London on 2nd - 3rd April 2020)
- ProgNET London 2020 (in London on 16th - 18th September 2020)
- Getting setup and started with doing AI on the Nvidia Jetson Nano (in London on 24th September 2019)
- Keynote by Zainab Ali on Abstract Data Types In The Region Of Abysmal Pain, And How To Navigate Them (in London on 30th September 2019)
- Revving up with Reinforcement Learning (SkillsCast recorded in September 2019)
- Machine Learning in action at Streetbees (SkillsCast recorded in September 2019)
Thanks to our sponsors
Tutorial: Bayesian Decision Making in The Face of Uncertainty
Gary Short
Gary Short is a freelance data scientist. He specialises in machine learning and predictive analytics on the Azure Platform, but has an interest in cloud scale analytics in all forms, especially computational linguistics and social network analysis.
SkillsCast coming soon.
When you make a decision, in an ideal world, you will have gathered all the pertinent facts and you will weigh them to come to a logical conclusion.
Sadly, you do not live in a perfect world and sometimes you will be forced to make a decision where there is a large degree of uncertainty. In this tutorial, Gary will teach you how to apply the Bayesian principles of machine learning in order to arrive at the best possible decision, by calculating the probability of events happening in an uncertain world. He’ll concentrate on decisions like, should you bet on Tiger Woods to win the Open? Is the mortgage rate likely to go up or down in the next quarter, and should you learn Go, or Rust?
Tutorial Prerequisites
The labs can be completed using either Excel or Python notebook, either via Anaconda installed locally, or a cloud based service such as Azure Notebooks. Attendees should come with/install their preference.
YOU MAY ALSO LIKE:
- Data Science for Fun and Profit (SkillsCast recorded in July 2016)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Fast Track to F# with Tomas Petricek (in London on 31st October - 1st November 2019)
- F# eXchange 2020 (in London on 2nd - 3rd April 2020)
- ProgNET London 2020 (in London on 16th - 18th September 2020)
- Getting setup and started with doing AI on the Nvidia Jetson Nano (in London on 24th September 2019)
- Keynote by Zainab Ali on Abstract Data Types In The Region Of Abysmal Pain, And How To Navigate Them (in London on 30th September 2019)
- Revving up with Reinforcement Learning (SkillsCast recorded in September 2019)
- Machine Learning in action at Streetbees (SkillsCast recorded in September 2019)
Thanks to our sponsors
Tutorial: Bayesian Decision Making in The Face of Uncertainty
Gary Short
Gary Short is a freelance data scientist. He specialises in machine learning and predictive analytics on the Azure Platform, but has an interest in cloud scale analytics in all forms, especially computational linguistics and social network analysis.