Probabilistic Graphical Models 3: Learning

Go to Course: https://www.coursera.org/learn/probabilistic-graphical-models-3-learning

Introduction

**Course Review: Probabilistic Graphical Models 3: Learning on Coursera** Are you fascinated by the intersection of statistics, computer science, and machine learning? If so, the "Probabilistic Graphical Models 3: Learning" course on Coursera may be the perfect fit for you. This course is the third installment in a series that dives deep into probabilistic graphical models (PGMs), a powerful framework for representing complex probability distributions among multiple interrelated random variables. **Course Overview** Probabilistic graphical models are not just theoretical constructs; they play a critical role in applications ranging from medical diagnosis to automated reasoning systems. This course focuses on the learning aspect of PGMs, covering various tasks and methodologies that will equip you with the knowledge to apply these models in real-world scenarios. **Syllabus Highlights** 1. **Learning: Overview** This initial module sets the stage by presenting the different learning tasks related to probabilistic graphical models. It's a great way to understand what to expect and how these models fit into the broader landscape of machine learning. 2. **Review of Machine Learning Concepts (Optional)** If you've taken Professor Andrew Ng's well-regarded Machine Learning class, this module will serve as a valuable recap of essential concepts. This optional review is particularly useful for those who want to ensure they have a solid foundation in the key principles of machine learning. 3. **Parameter Estimation in Bayesian Networks** This module tackles the basics of parameter estimation within Bayesian networks – an essential skill for anyone looking to work in PGM. You'll explore maximum likelihood estimation and the potential challenges it presents before moving on to Bayesian estimation techniques. The clarity with which these concepts are presented makes complex topics accessible. 4. **Learning Undirected Models** Transitioning from Bayesian networks to Markov networks, this module delves into the complexities of parameter estimation for undirected models. The introduction of the global partition function makes this section a bit more sophisticated. However, the content is well-structured, preparing learners for the intricacies of undirected graphical models. 5. **Learning BN Structure** Here, you'll learn to unravel and optimize the structure of Bayesian networks. By explaining how to formulate this learning as an optimization problem, the course effectively prepares you to navigate the challenges of graph complexity versus data fit. 6. **Learning BNs with Incomplete Data** One of the standout challenges in data science is dealing with incomplete datasets. This module introduces the Expectation Maximization (EM) algorithm, providing crucial insights that are applicable across various fields in statistics and machine learning. 7. **Learning Summary and Final** The course culminates in a summary of its core concepts and a final assessment that tests your understanding and application of the key topics covered in the course. 8. **PGM Wrap-up** Finally, the wrap-up module reflects on the methods covered throughout the course, discussing real-world trade-offs and practical applications of PGMs. This holistic view is invaluable as you contemplate how to leverage your newfound knowledge. **Who Should Take This Course?** This course is ideal for anyone interested in deepening their understanding of probabilistic models and their applications in machine learning. It's particularly beneficial for individuals looking to work in data science, artificial intelligence, or any field that relies on statistical modeling. If you have a good grasp of basic machine learning principles and are eager to explore PGMs in depth, this course is a stellar choice. **Conclusion: My Recommendation** I wholeheartedly recommend "Probabilistic Graphical Models 3: Learning" for those eager to delve into one of the most intellectually stimulating areas of machine learning. The course strikes a commendable balance between theory and practical application, making it a valuable learning experience. The instructors do an excellent job of breaking down complex topics into manageable segments, ensuring that participants are not overwhelmed. Take the plunge, enrich your understanding of PGMs, and apply these sophisticated concepts to tackle real-world problems in your field! Sign up on Coursera and discover the power of probabilistic graphical models today!

Syllabus

Learning: Overview

This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.

This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.

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.

In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.

This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.

In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.

This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final.

This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.