Probabilistic Programming With Python And Julia

Introduction and simple examples to start into probabilistic programming

What you'll learn
Introduction to probabilistic programming
Bayesian statistics
Markov Chain Monte Carlo
Gaussian Mixture Models
Bayesian Logistic Regression
Bayesian Linear Regression
Requirements
Python
Julia
Elementary understanding of statistics
Description
You want to know and to learn one of the top 10 most influencial algorithms of the 20th century? Then you are right in this course. We will cover many powerful techniques from the field of probabilistic programming. This field is fast-growing, because these technique are getting more and more famous and proof to be efficient and reliable. We will cover all major fields of Probabilistic Programming: Distributions, Markov Chain Monte Carlo, Gaussian Mixture Models, Bayesian Linear Regression, Bayesian Logistic Regression, and hidden Markov models.For each field, the algorithms are shown in detail: Their core concepts are presented in 101 lectures. Here, you will learn how the algorithm works. Then we implement it together in coding lectures. These are available for Python and Julia. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.Mastering this course will enable you to understand the concepts of probabilistic programming and you will be able to apply this in your private and professional projects.

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 Bayesian Statistics

Lecture 3 Distributions: Introduction

Lecture 4 Distributions: Uniform Distribution

Lecture 5 Distributions: Normal Distribution

Lecture 6 Distributions: Binomial Distribution

Lecture 7 Distributions: Poisson Distribution

Lecture 8 Monte Carlo Markov Chain

Section 2: Samplers

Lecture 9 Metropolis Hastings Sampling 101

Lecture 10 Metropolis Hastings Sampling Interactive 1

Lecture 11 Metropolis Hastings Sampling Interactive 2

Lecture 12 Metropolis Hastings Sampling Interactive 3

Section 3: Workspace Preparation

Lecture 13 Julia

Lecture 14 Python

Section 4: Gaussian Mixture Models

Lecture 15 GMM 101

Lecture 16 Kmeans 101

Lecture 17 GMM Coding (Julia)

Lecture 18 GMM Coding (Python)

Section 5: Bayesian Linear Regression

Lecture 19 Bayesian Linear Regression 101

Lecture 20 Bayesian Linear Regression Coding (Julia)

Lecture 21 Bayesian Linear Regression Coding (Python)

Section 6: Bayesian Logistic Regression

Lecture 22 Bayesian Logistic Regression 101

Lecture 23 Bayesian Logistic Regression Coding (Julia)

Lecture 24 Bayesian Logistic Regression Coding (Python)

Section 7: Bonus

Lecture 25 Congratulation and Thank you!

Lecture 26 Bonus lecture

Python and Julia users who like to learn probabilistic programming