Probabilistic Graphical Models 3: Learning

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

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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 third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

Course Syllabus

This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.

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Course Introduction

Probabilistic Graphical Models 3: Learning and Models This course is the third and last course in the specialization about Probabilistic Graphical Models (PDMs). These are state-of-the-art models that solve for a large number of parameters (loops) and are designed to span a wide variety of applications. We will learn two new modelling techniques, supervised (overlapping layers) and unsupervised (clustering). We will also learn how to use PDEs for clustering and for finding the best models. Finally, we will look at the challenges associated with clustering in the US and importances in Python programs. Note that the sample case study uses Python 2.7.Getting Started and Building Model Solvers Trains and Sends approximations Mean-Variance Analysis Discrete Optimization Probabilistic Graphical Models 1: Representation, Decomposition, and Visualization This course is the first course in a sequence that covers Probabilistic Graphical Models (PDMs), which are state-of-the-art models that solve for a large number of parameters (loops) and are designed to span a wide variety of applications. We will learn two new modelling techniques, variational (overlapping layers) and unsupervised (clustering). We will also learn how to use PDEs for clustering and for finding the best models.

Course Tag

Algorithms Expectation–Maximization (EM) Algorithm Graphical Model Markov Random Field

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