|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/probabilistic-programming-with-python-and-julia/
课程评论：没有评论
**课程名称：** 使用 Python 和 Julia 进行概率编程 **课程概述：** 本课程将带您深入了解 20 世纪十大最具影响力的算法之一——概率编程（Probabilistic Programming）。这个快速发展的领域因其强大、高效和可靠的技术而日益受到关注。课程将涵盖概率编程的各大核心领域，包括： * **概率分布 (Distributions)** * **马尔可夫链蒙特卡洛 (Markov Chain Monte Carlo - MCMC)** * **高斯混合模型 (Gaussian Mixture Models - GMMs)** * **贝叶斯线性回归 (Bayesian Linear Regression)** * **贝叶斯逻辑回归 (Bayesian Logistic Regression)** * **隐马尔可夫模型 (Hidden Markov Models - HMMs)** 对于每个领域，课程都将提供算法的详细讲解。首先，101 课程将介绍算法的核心概念，让您理解其工作原理。随后，将通过编程实践课程，引导您使用 Python 和 Julia 实现这些算法。 **学习成果：** 通过本课程的学习，您将能够： * 清晰地识别并定义所面临的问题。 * 制定有效的解决方案策略。 * 深入理解概率编程的核心概念。 * 将所学知识应用于您的个人和职业项目。 **目标受众：** 希望掌握概率编程技术，并将其应用于实际问题解决的学员。
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.
