Advanced Data Analysis and Bayesian Machine Learning in AI
Learning Path | 2 Course Series

Synopsis

Description

Bayesian statistics is a particular approach to applying probability to statistical problems. It provides us with mathematical tools to update our beliefs about random events in light of seeing new data or evidence about those events. Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. In particular Bayesian inference interprets probability as a measure of believability or confidence that an individual may possess about the occurance of a particular event. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Through this training we are going to apply Bayesian methods to A/B testing and also use adaptive algorithms to improve A/B testing performance. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Introduction:

At the outset of the course, participants are introduced to the project's overarching goals and objectives. This initial segment serves to provide a comprehensive understanding of the context in which the subsequent analyses and methodologies will be applied. Participants gain insight into the significance of the project and the potential impact of the techniques they will learn.

Data Exploration and Preparation:

Moving forward, participants delve into the dataset, embarking on a journey of exploration and preparation. Through guided exercises and hands-on activities, they gain a thorough understanding of the dataset's structure, historical trends, and demographic distributions. This segment equips participants with the necessary skills to preprocess and clean the data, ensuring it is well-suited for in-depth analysis.

Bayesian Table Analysis:

In this pivotal section, participants learn about Bayesian table analysis techniques, which enable them to derive nuanced insights from the dataset. By applying Bayesian principles, participants uncover hidden patterns and correlations within the data, gaining a deeper understanding of underlying relationships. Through practical examples and case studies, participants develop proficiency in leveraging Bayesian methods to extract valuable insights.

Bayesian Machine Learning and Multiple Variant Testing:

As the course progresses, participants advance to more sophisticated techniques, including Bayesian machine learning and multiple variant testing. They learn to implement Bayesian models using techniques such as Markov Chain Monte Carlo (MCMC) simulations, enabling them to make informed decisions based on experimental data. Through practical exercises and real-world examples, participants explore the practical applications of these techniques in various domains.

Conclusion:

In the final segment of the course, participants reflect on their journey and consolidate their learnings. They discuss the practical implications of the techniques they have acquired and contemplate how to apply them in their own professional contexts. This reflective exercise allows participants to synthesize their newfound knowledge and envision how they can leverage it to drive impactful outcomes in their future projects or research endeavors.