Probabilistic Programming with Python and Julia

Probabilistic Programming with Python and Julia

Introduction and simple examples to start into probabilistic programming

上次更新 7/2019

英语 [自动]

课程内容

7 个章节 • 26 个讲座 • 总时长 2 小时 39 分钟

https://www.udemy.com/course/probabilistic-programming-with-python-and-julia/

你将会学到的

Introduction to probabilistic programming

Bayesian statistics

Markov Chain Monte Carlo

Gaussian Mixture Models

Bayesian Logistic Regression

Bayesian Linear Regression

要求

Python

Julia

Elementary understanding of statistics

说明

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.



此课程面向哪些人：

Python and Julia users who like to learn probabilistic programming