Probabilistic Graphical Models 1: Representation

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

About Course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

Course Syllabus

In this module, we define the Bayesian network representation and its semantics. We also analyze the relationship between the graph structure and the independence properties of a distribution represented over that graph. Finally, we give some practical tips on how to model a real-world situation as a Bayesian network.

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Course Introduction

Probabilistic Graphical Models 1: Representation In this course you will continue your understanding of probabilistic graphical models, and learn more about computing the weights and biases of such models. You will also learn more about the representation scheme of such models. We will apply various techniques to represent the information in a graph, including embedding of nodes in their respective color schemes, and also how to segment the nodes to improve the parsing efficiency of a program. These techniques are implemented in a fairly simple yet powerful computational framework, and the results are presented in a very elegant and streamlined way. You will also learn about the underlying concepts of the machine learning algorithms, and how they are applied to solve problems in a practical way. After finishing this course, you will have gained a solid foundation to proceed with most future training examples in probabilistic graphical models and supervised machine learning problems. You will also have a solid base to go into any other course on algorithms in data science, machine learning, or any other domain in which you would like to learn how to use machine learning methods. Note that the machine learning techniques discussed in this course are not available for use in Deep Learning. Recommended Background: You should have at least one year programming experience under the "Know How" or equivalent experience (C, C++, Java, Python, etc.). You should have experience in programming algorithms, especially C++. You should be proficient at manipulating files, including a good understanding of algorithms in the C++ standard library.You should have

Course Tag

Bayesian Network Graphical Model Markov Random Field

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