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Bayesian Analysis with Python

  • Course Code: Artificial Intelligence - Bayesian Analysis with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to know Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Python Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to know Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the course, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the course you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. 

Working in a hands-on learning environment, led by our Bayesian Analysis with Python instructor, students will learn about and explore: 

  • A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ 
  • A modern, practical and computational approach to Bayesian statistical modeling 
  • A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Build probabilistic models using the Python library PyMC3 
  • Analyze probabilistic models with the help of ArviZ 
  • Acquire the skills required to sanity check models and modify them if necessary 
  • Understand the advantages and caveats of hierarchical models 
  • Find out how different models can be used to answer different data analysis questions 
  • Compare models and choose between alternative ones 
  • Discover how different models are unified from a probabilistic perspective 
  • Think probabilistically and benefit from the flexibility of the Bayesian framework 

Audience & Pre-Requisites 

This course is geared for attendees with Apache knowledge who wish to know Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ 

Pre-Requisites:  Students should have  

  • Basic to python Skills. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Thinking Probabilistically 
  • Thinking Probabilistically 
  • Statistics, models, and this course’s approach 
  • Probability theory 
  • Single-parameter inference 
  • Communicating a Bayesian analysis 
  • Posterior predictive checks 
  1. Programming Probabilistically 
  • Programming Probabilistically 
  • Probabilistic programming 
  • PyMC3 primer 
  • Summarizing the posterior 
  • Gaussians all the way down 
  • Groups comparison 
  • Hierarchical models 
  1. Modeling with Linear Regression 
  • Modeling with Linear Regression 
  • Simple linear regression 
  • Robust linear regression 
  • Hierarchical linear regression 
  • Polynomial regression 
  • Multiple linear regression 
  • Variable variance 
  1. Generalizing Linear Models 
  • Generalizing Linear Models 
  • Generalized linear models 
  • Logistic regression 
  • Multiple logistic regression 
  • Poisson regression 
  • Robust logistic regression 
  • The GLM module 
  1. Model Comparison 
  • Model Comparison 
  • Posterior predictive checks 
  • Occam’s razor – simplicity and accuracy 
  • Information criteria 
  • Bayes factors 
  • Regularizing priors 
  • WAIC in depth 
  1. Mixture Models 
  • Mixture Models 
  • Mixture models 
  • Finite mixture models 
  • Non-finite mixture model 
  • Continuous mixtures 
  1. Gaussian Processes 
  • Gaussian Processes 
  • Linear models and non-linear data 
  • Modeling functions 
  • Gaussian process regression 
  • Regression with spatial autocorrelation 
  • Gaussian process classification 
  • Cox processes 
  1. Inference Engines 
  • Inference Engines 
  • Inference engines 
  • Non-Markovian methods 
  • Markovian methods 
  • Diagnosing the samples 
  1. Where To Go Next? 
  • Where To Go Next? 
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