Probabilistic Graphical Models 2: Inference from Coursera

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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

What's inside

Syllabus

Inference Overview

This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference).

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Introduces students to probabilistic graphical models (PGMs), a powerful framework for encoding complex probability distributions for large, interconnected sets of variables, and using them to answer questions efficiently

Focuses on probabilistic inference, addressing how PGMs can be used to make predictions and answer questions, even when the distribution they represent is high-dimensional

Covers exact inference algorithms, such as variable elimination and belief propagation, as well as approximate inference algorithms, including sampling methods like Markov Chain Monte Carlo (MCMC) and loopy belief propagation (optional)

Provides hands-on programming assignments (optional) where students implement key routines of exact and approximate inference algorithms and apply them to real-world problems, reinforcing their understanding

Suitable for students with a background in probability, statistics, and machine learning, or those who have completed the first course in the PGM specialization

Reviews summary

Deep dive into pgm inference

According to learners, this course provides a deep dive into the inference techniques used in Probabilistic Graphical Models. Students widely praise the clarity of the lectures and the instructor's expertise. Many found the challenging programming assignments highly beneficial for solidifying their understanding, although some felt the course required significant prior knowledge in probability and linear algebra. It's considered an excellent course for building a strong theoretical foundation, though some reviews suggest it could benefit from more direct application examples.

Focuses heavily on theory and algorithms.

"Provides a strong theoretical understanding of inference algorithms."

"The course is very heavy on the mathematical and algorithmic theory."

"Excellent for understanding the 'how and why' behind the methods."

"This course leans more towards the theoretical side of PGM inference."

Programming assignments are demanding but helpful.

"The programming assignments were tough but very rewarding, really cemented the concepts."

"While difficult, the assignments were crucial for understanding the algorithms."

"The coding tasks require significant effort but are key to learning."

"The homework problems pushed my understanding significantly."

Instructor explains complex topics clearly.

"The lectures are very clear and easy to follow, explaining complex concepts simply."

"The instructor is great at breaking down difficult material."

"I found the explanations in the video lessons exceptionally clear."

"The professor's explanations were lucid and helped a lot with understanding."

Could use more real-world examples.

"Wish there were more practical examples or case studies demonstrating real-world use."

"Great theory, but less guidance on applying it to practical problems."

"It's a bit abstract; more hands-on application would be helpful."

Needs solid background in math and PGM1.

"Make sure you have a solid background in probability, linear algebra, and ideally PGM1."

"This course assumes you are comfortable with mathematical concepts and have taken the first part."

"The prerequisites are quite high; struggle without a strong math foundation."

"Definitely need to be proficient in probability and linear algebra coming into this."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Probabilistic Graphical Models 2: Inference with these activities:

Refresh linear algebra and probability concepts

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Strengthen your foundational understanding of linear algebra and probability concepts to support your learning in probabilistic graphical models (PGMs).

Browse courses on Linear Algebra

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Review textbooks or online resources on linear algebra.
Practice solving linear algebra problems, such as matrix operations and vector spaces.
Review concepts of probability theory, including random variables, distributions, and conditional probability.
Solve probability exercises and problems.

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Career center

Learners who complete Probabilistic Graphical Models 2: Inference will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist may use probabilistic graphical models to encode probability distributions over complex domains and answer questions about the data efficiently. This course covers exact and approximate algorithms for different types of inference tasks, which can help a Data Scientist build a foundation for developing and applying these models in real-world applications.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Probabilistic graphical models are a fundamental tool in formulating many machine learning problems. A Machine Learning Engineer may use these models to represent complex relationships between variables and make predictions or decisions. This course provides a deep understanding of inference algorithms for probabilistic graphical models, which can help a Machine Learning Engineer design and implement effective machine learning systems.

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Research Scientist

Research Scientists working in fields such as artificial intelligence, machine learning, and statistics may use probabilistic graphical models to develop new algorithms and theories for inference and decision-making. This course provides a comprehensive overview of exact and approximate inference algorithms, as well as their theoretical foundations, which can help a Research Scientist build a strong foundation for conducting research in this area.

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Software Engineer

Software Engineers working on machine learning or data science projects may use probabilistic graphical models to represent complex relationships between variables and make predictions or decisions. This course provides a practical understanding of inference algorithms for probabilistic graphical models, which can help a Software Engineer develop and implement efficient and scalable solutions.

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Quantitative Analyst

Quantitative Analysts use probabilistic models to analyze financial data and make investment decisions. Probabilistic graphical models are a powerful tool for representing complex relationships between financial variables and making predictions about future market behavior. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help a Quantitative Analyst develop and apply these models in the financial domain.

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Risk Analyst

Risk Analysts use probabilistic models to assess and manage risks in various domains, such as finance, insurance, and healthcare. Probabilistic graphical models can help Risk Analysts represent complex relationships between risk factors and make predictions about future events. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help a Risk Analyst develop and apply these models in risk management applications.

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Actuary

Actuaries use probabilistic models to assess and manage financial risks in the insurance industry. Probabilistic graphical models can help Actuaries represent complex relationships between risk factors and make predictions about future events. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help an Actuary develop and apply these models in insurance applications.

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Statistician

Statisticians use probabilistic models to analyze data and draw conclusions. Probabilistic graphical models are a powerful tool for representing complex relationships between variables and making inferences about the underlying population. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help a Statistician develop and apply these models in various statistical applications.

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Data Analyst

Data Analysts use probabilistic models to analyze data and extract insights. Probabilistic graphical models can help Data Analysts represent complex relationships between variables and make predictions about future events. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help a Data Analyst develop and apply these models in data analysis applications.

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Business Analyst

Business Analysts use probabilistic models to analyze data and make recommendations for business decisions. Probabilistic graphical models can help Business Analysts represent complex relationships between business factors and make predictions about future outcomes. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help a Business Analyst develop and apply these models in business analysis applications.

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Operations Research Analyst

Operations Research Analysts use probabilistic models to optimize complex systems. Probabilistic graphical models can help Operations Research Analysts represent complex relationships between system components and make predictions about future outcomes. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help an Operations Research Analyst develop and apply these models in optimization applications.

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Financial Analyst

Financial Analysts use probabilistic models to analyze financial data and make investment recommendations. Probabilistic graphical models can help Financial Analysts represent complex relationships between financial variables and make predictions about future market behavior. This course provides a foundation in inference algorithms for probabilistic graphical models, which can help a Financial Analyst develop and apply these models in financial analysis applications.

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Software Developer

Software Developers may use probabilistic graphical models to develop machine learning or data science applications. This course provides a practical understanding of inference algorithms for probabilistic graphical models, which can help a Software Developer implement efficient and scalable solutions.

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Data Engineer

Data Engineers may use probabilistic graphical models to develop and maintain data pipelines for machine learning or data science applications. This course provides a practical understanding of inference algorithms for probabilistic graphical models, which can help a Data Engineer design and implement efficient and reliable data pipelines.

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Product Manager

Product Managers may use probabilistic graphical models to understand user behavior and make product decisions. This course provides a high-level overview of inference algorithms for probabilistic graphical models, which can help a Product Manager make informed decisions about product development and marketing.

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