Last updated 7/2019
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 996.70 MB | Duration: 2h 39m
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
Homepage
https://www.udemy.com/course/probabilistic-programming-with-python-and-julia/
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